· faculty of computer science and information technology dam data analysis and machine learning...

Faculty of Computer Science and Information Technology

LIST OF COURSES FOR EXCHANGE STUDENTS

ACADEMIC YEAR 2019/2020

Faculty Faculty of Computer Science and Information Technology

Course code (if

applicable) Course title Person responsible for the course Semester

(winter/summer) ECTS points

CSA Computer System Architecture Ph.D. Eng. Mariusz Kapruziak winter/summer 4

FDC FPGA Design and Reconfigurable Computing Ph.D. Eng. Mariusz Kapruziak winter/summer 4

MDS Microprocessor Design and Soft-processors Ph.D. Eng. Mariusz Kapruziak winter/summer 5

EMS Embedded Systems Ph.D. Eng Mirosław Łazoryszczak winter/summer 4

ASP Audio Signal Processing Ph.D. Eng. Mirosław Łazoryszczak winter/summer 4

DCI Digital Circuits Ph.D. Eng. Mirosław Łazoryszczak winter/summer 4

MAD Mobile Application Development Ph.D. Eng. Radosław Maciaszczyk winter/summer 4

FEC Fundamentals of Error-Correcting Block Codes Ph.D. Dorota Majorkowska-Mech summer 3

CTN Computer and Telecommunication Networks

Ph.D. Eng. Remigiusz Olejnik winter/summer 4

LAT LaTeX – Document Preparation System for Engineers Ph.D. Eng. Remigiusz Olejnik winter/summer 2

ARD Arduino – an Introduction to the Internet of Things Ph.D. Eng. Remigiusz Olejnik winter/summer 6

BCL Bash – Command Language Interpreter for Engineers

Ph.D. Eng. Magdalena Szaber-Cybularczyk winter/summer 2

AOC Алгоритмические основы цифровой обработки сигналов и изображений

Prof. Aleksandr Cariow winter/summer 4

APR

АЛГОРИТМИЧЕСКИЕ ПРИЁМЫ И ТРЮКИ В ЦИФРОВОЙ ОБРАБОТКЕ СИГНАЛОВ И ИЗОБРАЖЕНИЙ

Prof. Aleksandr Cariow winter/summer 5


DAM Data Analysis and Machine Learning Ph.D. Hab. Eng. Przemysław Klęsk winter/summer 3

DMA Data Mining Algorithms Ph.D. Hab. Eng. Przemysław Klęsk winter/summer 4

IAI Introduction to Artificial Intelligence Ph.D. Hab. Eng. Przemysław Klęsk winter/summer 3

MIG Methods of Artificial Intelligence in Games Ph.D. Hab. Eng. Przemysław Klęsk winter/summer 3

CVI Computer Vision and Fast Object Detection Ph.D. Hab. Eng. Przemysław Klęsk winter/summer 4

PD1 Programmable Control Devices 1 – Logic Control Systems Ph.D. Eng. Sławomir Jaszczak winter/summer 5

PD2 Programmable Control Devices 2 – Continuous Control Systems Ph.D. Eng. Sławomir Jaszczak summer 5

ESY Expert Systems Ph.D. Eng. Joanna Kołodziejczyk winter/summer 4

PPA Prolog Programming for Artificial Intelligence Ph.D. Eng. Joanna Kołodziejczyk winter/summer 4

BIA Biologically Inspired Algorithms Ph.D. Eng. Joanna Kołodziejczyk winter/summer 6

RPL Ruby Programming Language Ph.D. Eng. Joanna Kołodziejczyk winter/summer 3

RRF Ruby on Rails Framework for Web Development Ph.D. Eng. Joanna Kołodziejczyk winter/summer 3

ANN Artificial Neural Networks and their Application in System Modeling Ph.D. Eng. Marcin Pluciński winter/summer 3

SEC Software for Engineering Calculations Ph.D. Eng. Marcin Pluciński winter/summer 2

EFL Essentials of Fuzzy Logic and its Application to System Modeling and Control

Ph.D. Eng. Marcin Pluciński winter/summer 6

KED Knowledge Extraction from Data with Rough Set Method and its Applications

Ph.D. Eng. Marcin Pluciński winter/summer 3

CPL C# Programming Language M.Sc. Eng. Marcin Pietrzykowski winter/summer 4

HMM Hidden Markov Models and its Applications M.Sc. Eng. Marcin Pietrzykowski winter/summer 3


FPL F# Programming Language M.Sc. Eng. Marcin Pietrzykowski winter/summer 4

GUI Graphical User Interface in .NET M.Sc. Eng. Marcin Pietrzykowski winter/summer 3

IDS Intelligent Decision Systems M.Sc. Eng. Wojciech Sałabun winter/summer 6

IMP Intro to Mathematical Programming M.Sc. Eng. Wojciech Sałabun winter/summer 6

A3P Arduino 3D Printer M.Sc. Eng. Wojciech Sałabun winter/summer 6

IST Intro to Statistic: Making Decisions Based on Data M.Sc. Eng. Wojciech Sałabun winter/summer 6

MAT MATLAB Programming M.Sc. Eng. Wojciech Sałabun winter/summer 6

COM Compilers Prof. Włodzimierz Bielecki winter/summer 6

PAP Parallel Programming Prof. Włodzimierz Bielecki winter/summer 6

C++ C++ Programming Language Ph.D. Eng. Agnieszka Konys winter/summer 4

KEO Knowledge Engineering and Ontology Development Ph.D. Eng. Agnieszka Konys winter/summer 4

CPR Cloud Programming Ph.D. Eng. Łukasz Radliński winter 4

SEN Software Engineering Ph.D. Eng. Łukasz Radliński winter 4

JAV Java programming Ph.D. Eng. Tomasz Wierciński winter/summer 6

SFP Spring Framework Programming Ph.D. Eng. Tomasz Wierciński winter/summer 6

DDO Dynamic Documents and Front-end Web Development

Ph.D. Hab. Eng. Jarosław Jankowski winter 3

ECO E-commerce and Online Marketing Technologies

Ph.D. Hab. Eng. Jarosław Jankowski winter 3

CSI Computer Simulation Ph.D. Hab. Eng. Przemyslaw Korytkowski winter/summer 6


DCM Digital Color Management Ph.D. Hab. Eng. Przemyslaw Korytkowski winter/summer 6

DSY Database Systems Ph.D. Hab. Eng. Przemyslaw Korytkowski winter/summer 6

BCI Brain-Computer Interface Ph.D. Hab. Izabela Rejer winter/summer 4

EEG EEG Signal Analysis in Matlab Ph.D. Hab. Izabela Rejer winter/summer 4

JSW Introduction to JavaScript web application development

Ph.D. Eng. Bartłomiej Małachowski winter/summer 3

ANG Web application development with Angular framework

Ph.D. Eng. Bartłomiej Małachowski winter/summer 4

MBV Management and Business Communication Virtualisation Ph.D. Eng. Piotr Sulikowski summer 3

BIN Business Intelligence Ph.D. Hab. Eng. Przemysław Różewski

winter/summer 5

DWB Data Warehousing and Big Data Ph.D. Hab. Eng. Przemysław Różewski

winter/summer 5

CGP Computer Games Programming Ph.D. Hab. Eng. Radosław Mantiuk summer 4

GUA Graphics user interfaces in Android Ph.D. Hab. Eng. Radosław Mantiuk winter/summer 4

3DD 3D printing and design Ph.D. Hab. Eng. Radosław Mantiuk winter/summer 4

DLV Deep learning for visual computing Ph.D. Hab. Eng. Radosław Mantiuk winter/summer 5

CPH Computational photography Ph.D. Hab. Eng. Radosław Mantiuk winter/summer 3

CVS Computer Vision for Video Surveillance Ph.D. Eng. Adam Nowosielski winter/summer 3

HCI Human-Computer Interaction Ph.D. Eng. Adam Nowosielski winter/summer 3

FIRST DEGREE (BACHELOR)

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Field of study

Mode of study

Graduate's qualification

ECTS

Form of course credit

Code

Electives

Language

Elective group

Wymiana międzynarodowa

stationary

WI-1-3DD

4,0

credits english

ECTS (forms) 4,0

Level first cycle

Area(s) of study

Educational profile -

Module

Course unit 3D printing and design

Field of specialisation

Administering faculty Katedra Systemów Multimedialnych

Form of instruction Semester ECTS Weight CreditCode Hours

project course 1W, 2S 30 3,0 0,75 creditsP

lecture 1W, 2S 15 1,0 0,25 creditsW

Mantiuk Radosław ([email protected])Leading teacher

Mantiuk Radosław ([email protected])Other teachers

PrerequisitesW-1 Without any initial requirements

Module/course unit objectivesC-1 Transfer of knowledge and gaining skills and competences in the field of CD model preparation and ctual 3D printing.

Course content divided into various forms of instruction Number of hours

T-P-1 Implementation of a project involving the design of the object in CAD software, preparation of the g-code in CAM software, and printing the object on the FFF printer. 30

T-W-1 3D printing technologies. 2

T-W-2 FFF printer construction. 2

T-W-3 Introduction to CAD software. 2

T-W-4 Tutorial: designing basic object. 3

T-W-5 CAD techniques. 2

T-W-6 Introduction to CAM software. 2

T-W-7 Object manufacturing. 2

Student workload - forms of activity Number of hoursParticipation in workshops. 30A-P-1

Development of the CAD model as a part of homework. 60A-P-2

Participation in lectures. 15A-W-1

Learning to pass the exam. 15A-W-2

Teaching methods / toolsM-1 Lecture

M-2 Workshops

Evaluation methods (F - progressive, P - final)S-1 Preparation of the CAD model and physical print of this model.P

Designed learning outcomesReference to the

learning outcomesdesigned for the fields of

study

Reference tolearning outcomes

leading to thedegree of "inżynier"

Reference to thelearning outcomes

defined for theparticular areas of

education

Teachingmethods

Courseobjectives Course content Evaluation

methods

Knowledge

C-1 S-1T-W-1T-W-2T-W-3T-W-4

M-1WM-WI_1-_??_W01Gaining knowledge on 3D printing and design

T-W-5T-W-6T-W-7

3D printing and design

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Skills

C-1 S-1T-P-1 M-2WM-WI_1-_??_U01Gaining skills on 3D printing and design

Other social / personal competences

C-1 S-1T-P-1 M-2WM-WI_1-_??_K01Gaining competences on 3D printing and design

Outcomes Grade Evaluation criterion

KnowledgeWM-WI_1-_??_W01 2,0

3,0 Finished printout

3,54,04,55,0

SkillsWM-WI_1-_??_U01 2,0


3,54,04,55,0

Other social / personal competencesWM-WI_1-_??_K01 2,0


3,54,04,55,0

Required reading1. Ben Redwood, Brian Garret, Filemon Schöffer, and Tony Fadell, The 3D Printing Handbook: Technologies, Design and Applications,Google Book, 2018

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Field of study

Mode of study


ECTS


Code

Electives

Language

Elective group


stationary

WI-1-A3P

6,0

credits english

ECTS (forms) 6,0

Level first cycle

Area(s) of study


Module

Course unit Arduino 3D Printer


Administering faculty Katedra Metod Sztucznej Inteligencji i MatematykiStosowanej


laboratory course 1W, 2S 30 3,0 0,50 creditsL


Sałabun Wojciech ([email protected])Leading teacher

Jaszczak Sławomir ([email protected])Other teachers

PrerequisitesW-1 None

Module/course unit objectivesC-1 Acquire the basic knowledge on Arduino platform

C-2 The practical skills of the hardware and software synthesis by using Arduino

Course content divided into various forms of instruction Number of hoursT-L-1 Arduino - writing simple program 10

T-L-2 Arduino project - Hardware and Software synthesis 19

T-L-3 Exam 1

T-W-1 History of Arduino 2

T-W-2 Arduino - official boards 6

T-W-3 Arduino - shields 6

T-W-4 Arduino - programming 15

T-W-5 Exam 1

Student workload - forms of activity Number of hoursThe attendance in the labolatories 30A-L-1

The individual work of a student 60A-L-2

The attendance in the lectures 30A-W-1

The individual work of a student 60A-W-2

Teaching methods / toolsM-1 Informative lectures

M-2 Discussion

M-3 Laboratories with computers

Evaluation methods (F - progressive, P - final)S-1 The discussion summing up the knowledge gained during the lecturesF

S-2 Written examP



study





education

Teachingmethods


methods

Knowledge

Arduino 3D Printer

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C-1 S-2T-W-1T-W-2 M-1

M-2WM-WI_1-_null_W01After the lectures the student will be able to describe Arduinoboards and shields

T-W-3T-W-4

Skills

C-2 S-1T-L-1 M-2M-3

WM-WI_1-_null_U01The student will be able to write program for Arduino platform

T-L-2



KnowledgeWM-WI_1-_null_W01 2,0

3,0 The student is able to define and describe Arduino boards and shields

3,54,04,55,0

SkillsWM-WI_1-_null_U01 2,0

3,0 The student is able to make hardware and software synthesis by using Arduino platform

3,54,04,55,0


Required reading1. Scientific papers and materials provided by the lecturer

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Field of study

Mode of study


ECTS


Code

Electives

Language

Elective group


stationary

WI-1-ARD

6,0

credits english

ECTS (forms) 6,0

Level first cycle

Area(s) of study


Module

Course unit Arduino – an introduction to the Internet of Things


Administering faculty Katedra Architektury Komputerów i Teleinformatyki




Olejnik Remigiusz ([email protected])Leading teacher

Other teachers

PrerequisitesW-1 Basics of: C programming, electronics and computer systems architecture.

Module/course unit objectives

C-1To gain:1. theoretical and practical skills in Arduino programming,2. ability of advanced hardware projects preparation.


T-L-1

1. Introduction to Arduino, its hardware and software design, IDE.2. The art of Arduino programming – sketch and its structure: setup(), loop(), comments; data types;variables; arithmetic, logical, conditional, relational, increment operators; constants; functions; flowcontrol: if, if...else, for, while, do...while; arrays; strings; digital I/O; analog I/O; time; math; random;serial communication; libraries; PWM; interrupts; I2C; SPI; SD card; wired and wireless networking.3. Detailed overview of all sensors that will be used during laboratory.4. Examples built-in the IDE. Hello world! sketch.5. Using of breadboard, resistors and LEDs, buttons, switches, digital inputs, analog inputs, digitaloutputs, PWM.6. Light: LED, fading LED, 2-color LED, RGB LED, LED bar graph, 7-digits LED display, dot-matrix LEDdisplay, LCD display.7. Sensors: humidity, temperature, pressure, raindrops, PIR, ultrasonic, sound, knock, vibration, photoresistor, tilt, infrared, Hall magnetic, rotary encoder, flame, joystick, metal touch, mercury switch,detection of gases, 3D accelerometer, obstacle avoidance IR, optical broken light, laser.8. Outputs: motor control: DC motor, servo motor, stepper motor; relay module9. Sound: tone library, microphone, buzzer, speaker.10. Analog and digital inputs: reading analog voltage, external keyboard and mouse.11. RFID module, SD storage, GPS receiver.12. Ethernet shield, wireless communication.

30

T-P-1Implementation of selected problem:1. Hardware design proposal.2. Software implementation of the problem's solution.3. Preparation of the project's documentation.

30

Student workload - forms of activity Number of hoursAttendance in the classes 30A-L-1

Preparation for the classes 14*2 h 28A-L-2

Preparation of the report 14*2 h 28A-L-3

Consultations to the laboratory work 4A-L-4

Attendance in the classes 30A-P-1

Completing of the project 60A-P-2

Teaching methods / toolsM-1 Laboratory work and project

Arduino ? an introduction to the Internet of Things

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Evaluation methods (F - progressive, P - final)

S-1 Laboratory – evaluation of the reports submitted after each classProject – evaluation of the final project, along with its documentationP



study





education

Teachingmethods


methods

Knowledge

SkillsWM-WI_1-_??_U01Student will gain theoretical and practical skills in Arduinoprogramming, along with ability of advanced hardware projectspreparation



Knowledge


3,03,54,04,55,0


Required reading1. Michael Margolis, Arduino cookbook, O’Reilly, 2013

2. John Boxall, Arduino workshop: a hands on introduction with 65 projects, No Starch Press, 2013

3. Arduino Home https://www.arduino.cc/

Supplementary reading1. Adeel Javed, Building Arduino projects for the Internet of Things: experiments with real-world applications, Apress, 2016

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Field of study

Mode of study


ECTS


Code

Electives

Language

Elective group


stationary

WI-1-ANN

3,0

credits english

ECTS (forms) 3,0

Level first cycle

Area(s) of study


Module

Course unit Artificial neural networks and their application insystem modeling






Pluciński Marcin ([email protected])Leading teacher

Other teachers

PrerequisitesW-1 Basics of algebra and mathematical analysis.

W-2 Basics of computer science.

Module/course unit objectivesC-1 Extending of the knowledge about artificial neural networks, their construction, operation and learning techniques.

C-2 Gaining practical skills in the application of neural networks to solve real tasks of modeling and classification.

C-3 Familiarization with the software that could be used in tasks of modeling and classification using neural networks.

Course content divided into various forms of instruction Number of hoursT-L-1 Application of simple perceptron neural network to solve classification tasks. 2

T-L-2 Application of feed-forward multilayer neural networks to solve complex real tasks of classification. 2

T-L-3 Application of feed-forward multilayer neural network in modeling (real technical, economic andmedical problems). 2

T-L-4 Applications of RBF neural networks in modeling of technical and economic problems. 2

T-L-5 Application of unsupervised learning networks to the data clustering problem. 2

T-L-6 Hopfield network - application to the pattern recognition problem. 2

T-L-7 Final work. 3

T-W-1 The introduction to neural networks. Feed-forward neural networks. The structure and operation of theartificial neuron. 2

T-W-2 Simple Perceptron network - structure and learning methods. Example of learning and action of thenetwork. Selected applications of the Perceptron network. 2

T-W-3Feed-forward multilayer neural networks. Network learning methods - backpropagation. Examples oflearning and operation of the network. Selected network applications. Selection of the optimal networkarchitecture.

3

T-W-4 Neural networks with radial basis function - RBF neural networks. Structure and learning methods.Examples of applications. Probabilistic neural networks. 3

T-W-5 Self-organizing networks - unsupervised learning algorithms. The strucrure and operation of networks.Kohonen's network and learning algorithm. Examples of applications of self-organizing networks. 2

T-W-6 Recursive networks - Hopfield network, Hamming network. Construction, operation, learning methods.Examples of network applications. 2

T-W-7 Evaluation of knowledge. 1

Student workload - forms of activity Number of hoursParticipation in labs. 15A-L-1

Developement of programs and preparation of reports on the lab activity. 45A-L-2

Participation in lectures and evaluation. 15A-W-1

Artificial neural networks and their application in system modeling

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Student workload - forms of activity Number of hoursSelf preparing to final evaluation. 12A-W-2

Realization of homework. 3A-W-3

Teaching methods / toolsM-1 Lecture with presentation.

M-2 Labs - self-realization of tasks with the application of neural networks. Work will be done using Matlab ANN Toolbox and self-developed software.

Evaluation methods (F - progressive, P - final)S-1 Lecture: written test.P

S-2 Laboratory: evaluation of tasks carried out during the classes.F

S-3 Laboratory: evaluation of reports.F

S-4 Laboratory: evaluation of final work.P



study





education

Teachingmethods


methods

Knowledge

C-1 S-1T-W-1T-W-2T-W-3

M-1WM-WI_1-_??_W01The student knows the types of artificial neural networks, theirstructure, operation and ways of learning.

T-W-4T-W-5T-W-6

C-1 S-1T-W-1T-W-2T-W-3

M-1WM-WI_1-_??_W02The student knows practical applications of specific types ofartificial neural networks.

T-W-4T-W-5T-W-6

Skills

C-2C-3

S-2S-3S-4

T-L-1T-L-2T-L-3T-L-4

M-2WM-WI_1-_??_U01The student has the ability to solve practical problems(economic, technical and other) using artificial neural networks.

T-L-5T-L-6T-L-7




3,0 The student knows the types of artificial neural networks, their structure, operation and ways of learning at the basic level.

3,54,04,55,0

WM-WI_1-_??_W02 2,03,0 The student knows practical applications of specific types of artificial neural networks at the basic level.

3,54,04,55,0


3,0 The student has the ability to solve practical problems (economic, technical and other) using artificial neural networks at thebasic level.

3,54,04,55,0


Required reading1. David Kriesel, A Brief Introduction to Neural Networks, 20122. James A. Freeman, David M. Skapura, Neural Networks: Algorithms, Applications, and Programming Techniques, Addison-WesleyPublishing Company, 2005

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Field of study

Mode of study


ECTS


Code

Electives

Language

Elective group


stationary

WI-1-ASP

3,0

credits english

ECTS (forms) 3,0

Level first cycle

Area(s) of study


Module

Course unit Audio Signal Processing






Łazoryszczak Mirosław ([email protected])Leading teacher

Other teachers

PrerequisitesW-1 Basics of programming and signal processing.

Module/course unit objectivesC-1 Getting familiar with basic issues and selected methods of sound processing.

Course content divided into various forms of instruction Number of hoursT-L-1 Audio signal generating and manipulating using selected programming tools. 4

T-L-2 Creating simple GUI framework for audio processing 2

T-L-3 Selected digital filter implementation 2

T-L-4 Audio effects implementation eg. delay, echo, pitch shift etc. 4

T-L-5 Music pitch retrieval methods 3

T-W-1 Basic of sound. Audio perception. 2

T-W-2 Acoustical signal acquisition. Transducers – microphones and speakers. 2

T-W-3 Home recording studios: acoustics and equipment 2

T-W-4 Audio signal representations and sound analysis. 2

T-W-5 Digital filters. 2

T-W-6 Sound effects. Sound modeling and synthesis. 2

T-W-7 Selected applications of audio processing eg. noise reduction, automatic recognition of music. 2

T-W-8 Written exam 1

Student workload - forms of activity Number of hoursLabs attendance 15A-L-1

Labs and reports preparation 43A-L-2

Laboratory consultations 2A-L-3

Classes attendance 15A-W-1

Preparation to the exam 13A-W-2

Consultation to lectures 2A-W-3

Teaching methods / toolsM-1 Presentation lecture

M-2 Laboratory work

Evaluation methods (F - progressive, P - final)S-1 Lecture - written examF

Audio Signal Processing

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Evaluation methods (F - progressive, P - final)S-2 Labs - written reportsF



study





education

Teachingmethods


methods

Knowledge

C-1 S-1T-W-1T-W-2T-W-3T-W-4

M-1WM-WI_1-_??_W01The student knows the basic attributes of audio signals, theways of their perception and selected processing methods.

T-W-5T-W-6T-W-7

Skills

C-1 S-2T-L-1T-L-2T-L-3

M-2WM-WI_1-_??_U01The student is able to implement basic problems of soundprocessing using the selected programming language.

T-L-4T-L-5




3,0 Student describes sound signals attributes, is familiar with perception rules, knows principles of selected processingmethods.

3,54,04,55,0


3,0 Student is able to generate, manipulate, analyze and process audio signals in basic way.

3,54,04,55,0


Required reading1. Rocchesso D., Introduction to Sound Processing, Verona, 2003, https://archive.org/download/IntroductionToSoundProcessing/vsp.pdf

Supplementary reading1. Zoelzer U. (ed.), DAFX – Digital Audio Effects, Wiley, 2002

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Field of study

Mode of study


ECTS


Code

Electives

Language

Elective group


stationary

WI-1-BCL

2,0

credits english

ECTS (forms) 2,0

Level first cycle

Area(s) of study


Module

Course unit Bash – Command Language Interpreter for Engineers






Szaber-Cybularczyk Magdalena ([email protected])Leading teacher

Other teachers


Module/course unit objectivesC-1 Practical skills, allowing the user to type command and scripts which cause actions.

Course content divided into various forms of instruction Number of hoursT-L-1 Basic commands for files and directories 3

T-L-2 Redirection, Expansion and Quoting 2

T-L-3 Positional parameters and arthmetics operations 2

T-L-4 Array 2

T-L-5 Flow control 4

T-L-6 Functions 1

T-W-1 What's BASH? Basic commands for files and directories 3

T-W-2 How to make a script? Using the most popular text-editors. 2

T-W-3 How it works : redirection, expansion, quoting, positional parameters and array 4

T-W-4 Flow Control : Branching with if and case, looping with while/until and for 4

T-W-5 How to write a function ? 2

Student workload - forms of activity Number of hoursLaboratories 15A-L-1

Lectures 15A-W-1

Teaching methods / toolsM-1 Laboratories

M-2 Lectures

Evaluation methods (F - progressive, P - final)S-1 Continuous assessment.F



study





education

Teachingmethods


methods

Knowledge

SkillsWM-WI_1-_??_U01Student will gain practical skills in Bash.

Bash ? Command Language Interpreter for Engineers

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Knowledge


3,03,54,04,55,0


Required reading1. 1. Carl Albing, JP Vossen, Cameron Newham, Bash Cookbook: Solutions and Examples for bash Users, O'Reilly, 2007

2. Cameron Newham and Bill Rosenblatt, Learning the Bash Shell, O'Reilly, 2005

Supplementary reading1. http://www.gnu.org/software/bash/manual/

2. http://tldp.org/LDP/abs/html/

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Field of study

Mode of study


ECTS


Code

Electives

Language

Elective group


stationary

WI-1-BIA

6,0

credits english

ECTS (forms) 6,0

Level first cycle

Area(s) of study


Module

Course unit Biologically inspired algorithms






Kołodziejczyk Joanna ([email protected])Leading teacher

Other teachers

PrerequisitesW-1 Basic programming skills

Module/course unit objectivesC-1 To introduce and discuss algorithm that were inspired by biological phenomenon (part of Artificial Intelligence domain).

C-2 Application of different algorithms in various real and test problems

Course content divided into various forms of instruction Number of hoursT-L-1 Optimization - simple heuristics 2

T-L-2 Genetic algorithm implementation 4

T-L-3 Evolution strategies implementation 4

T-L-4 Particle Swarm Optimization algorithm implementation 2

T-L-5 Differential evolution implementation 2

T-L-6 Ant colony optimization for discrete problems - implementation 2

T-L-7 Immune systems - Clonalg, anomaly detection 4

T-L-8 Neural networks - supervised learning - implementation 3

T-L-9 Neural network - usupervised 3

T-L-10 Hybrid solutions - implementation 4

T-W-1 Computation intelligence - introduction 2

T-W-2 Evolutionary algorithm 4

T-W-2 Optimization task - chalanges 2

T-W-4 Evolution strategies 2

T-W-5 Differential evolution 2

T-W-6 Particle Swarm Optimization as a robust optimization method in continues domain 2

T-W-7 Ant colony optimization for discrete problems. 2

T-W-8 Artificial Immune Systems as an optimization tool 4

T-W-9 Neural networks - supervised 5

T-W-10 Neural networks - unsupervised 3

T-W-11 Hybrid methaheuristics 2

Student workload - forms of activity Number of hoursParticipation in labs 30A-L-1

Homeworks - algorithms implementation, analysis, raports 50A-L-2

Biologically inspired algorithms

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Student workload - forms of activity Number of hoursSelfstuying - reading 10A-L-3

Lectures participation 30A-W-1

Literature/articles reading 30A-W-2

Preparing to the test 30A-W-3

Teaching methods / toolsM-1 Lecture with presentation and conversation

M-2 Software development.

Evaluation methods (F - progressive, P - final)S-1 Quiz checking the knowlage on biologicaily inspired algorrithms.P

S-2 Examination of programming tasksF



study





education

Teachingmethods


methods

Knowledge

C-1 S-1

T-W-1T-W-2T-W-4T-W-5T-W-6

M-1WM-WI_1-_null_W01Student will know how to apply different algorithms and will beaware of the power, and the limitations, of discussed during thecourse methods.

T-W-7T-W-8T-W-9T-W-10T-W-11

Skills

C-2 S-2

T-L-1T-L-2T-L-3T-L-4T-L-5

M-2WM-WI_1-_null_U01Practical skills of implementing, analysing and testingalgorithms described during the course.

T-L-6T-L-7T-L-8T-L-9T-L-10




3,0 Students can describe how the algorithms discussed during the cours works.

3,54,04,55,0


3,0 Implementation of algorithms basic variants

3,54,04,55,0


Required reading1. Thomas Weise, Global Optimization Algorithms - Theory and Application, online book, 2011

[ logo uczelni ]

Field of study

Mode of study


ECTS


Code

Electives

Language

Elective group


stationary

WI-1-BCI

4,0

credits english

ECTS (forms) 4,0

Level first cycle

Area(s) of study


Module

Course unit Brain-Computer Interface


Administering faculty Katedra Inżynierii Systemów Informacyjnych




Rejer Izabela ([email protected])Leading teacher

Rejer Izabela ([email protected])Other teachers



C-1 To provide the knowledge about EEG devices, the features of EEG data, and the methods for transforming EEG data tosignals used for controling brain computer interfaces.

C-2 To equip the students with the ability of designing and programming interfaces controlling the external devices with brainwaves.

Course content divided into various forms of instruction Number of hoursT-L-1 The applications for EEG data analysis. 6

T-L-2 Tests of different EEG devices. 8

T-L-3 Creating a BCI for a given control task. 19

T-L-4 Testing the interface with real users. 10

T-L-5 Exam. 2

T-W-1 Brain Computer Interface (BCI) - the main paradigms. 4

T-W-2 The main parts of a human brain. 2

T-W-3 The main structure of BCI 3

T-W-4 Controling external devices with BCI. 2

T-W-5 Methods for EEG data preprocessing, feture extraction and classification used in BCI. 2

T-W-6 Exam. 2

Student workload - forms of activity Number of hoursThe attendence in the laboratories. 45A-L-1

The individual work of a student. 45A-L-2


The individual work of a student. 15A-W-2

Teaching methods / toolsM-1 Informative lectures.

M-2 Discussion.

M-3 Laboratories with computers and EEG devices.

Evaluation methods (F - progressive, P - final)S-1 The final report describing the created interface, tests results, and the conclusions.P

S-2 The final discussion summing up the knowlegde gained during the lectures.P

Brain-Computer Interface

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study





education

Teachingmethods


methods

Knowledge

C-1 S-2

T-W-1T-W-2T-W-3 M-1

M-2

WM-WI_1-_null_W01After the lectures the student will be able to: define a BCI,describe the main problems with EEG data, describe the EEGdevice, descibe different BCI paradigms, choose the processingmethods suitable for different paradigms and different EEGdata.

T-W-4T-W-5T-W-6

Skills

C-2 S-1T-L-1T-L-2T-L-3

M-3WM-WI_1-_null_U01The student will be able to create the project of a BCI suitablefor a given task.

T-L-4T-L-5




3,0 The student is able to define the main BCI concepts.

3,54,04,55,0


3,0 The student is able to create a general project of a BCI.

3,54,04,55,0


Required reading1. Lotte F., Study of Electroencephalographic Signal Processing and Classification Techniques towards the use of Brain-ComputerInterfaces in Virtual Reality Applications, 2008, PhD Thesis, https://sites.google.com/site/fabienlotte/phdthesis

[ logo uczelni ]

Field of study

Mode of study


ECTS


Code

Electives

Language

Elective group


stationary

WI-1-BIN

5,0

credits english

ECTS (forms) 5,0

Level first cycle

Area(s) of study


Module

Course unit Business Intelligence






Różewski Przemysław ([email protected])Leading teacher

Other teachers

PrerequisitesW-1 SQL basics, basic understanding of business processes

Module/course unit objectivesC-1 Understanding key concepts and tools in business intelligence, data analysis, and data visualization.

Course content divided into various forms of instruction Number of hoursT-L-1 Analysis of Multiple Business Perspectives 12

T-L-2 Dashboard Design in PowerBI 18

T-W-1 Business Intelligence Concepts 3

T-W-2 Business Analytics Fundamentals 5

T-W-3 Data Description and Visualization 8

T-W-4 Dashboard Design 10

T-W-5 Business Performance Management Systems 4

Student workload - forms of activity Number of hoursuczestnictwo w zajęciach 30A-L-1

Homework 45A-L-2

uczestnictwo w zajęciach 30A-W-1

Homework 45A-W-2


M-2 Cases studies

Evaluation methods (F - progressive, P - final)S-1 ProjectP

S-2 Written examP



study





education

Teachingmethods


methods

KnowledgeWM-WI_1-_??_W01Understanding key concepts in business intelligence, dataanalysis, and data visualization

Skills

Business Intelligence

[ logo uczelni ]

WM-WI_1-_??_U01Be able to effective use Data Visualization and Dashboard tool.




3,03,54,04,55,0


3,03,54,04,55,0


Required reading1. Grossmann, Wilfried, Rinderle-Ma, Stefanie, Fundamentals of Business Intelligence, Springer-Verlag Berlin Heidelberg, 2015, DOI:10.1007/978-3-662-46531-8

[ logo uczelni ]

Field of study

Mode of study


ECTS


Code

Electives

Language

Elective group


stationary

WI-1-CPL

4,0

credits english

ECTS (forms) 4,0

Level first cycle

Area(s) of study


Module

Course unit C# Programming Language






Pietrzykowski Marcin ([email protected])Leading teacher

Other teachers


Module/course unit objectivesC-1 Familiar with the sytnax, structures and principles used in the c# language

C-2 The ability to develop an object-oriented program in c# language.

Course content divided into various forms of instruction Number of hoursT-L-1 Introduction to visual Studio IDE and C# 2

T-L-2 Data types, operators 2

T-L-3 Controlling Programmatic Flow 2

T-L-4 Exceptions 2

T-L-5 Constructing Complex Types: classes and structs 4

T-L-6 Inheritance, Abstraction, Object Interfaces 4

T-L-7 Generic Types 2

T-L-8 Generic Collections 2

T-L-9 Input-output operations 2

T-L-10 Threading, parallelism and asynchronous operations 4

T-L-11 Windows Forms Applications 4

T-W-1 Introduction to: Object Oriented Programming, Managed Languages and C# 2

T-W-2 Controlling Programmatic Flow, Manipulating Types 2

T-W-3 Constructing Complex Types, Object Interfaces and Inheritance 4

T-W-4 Generic Types and Collections 2

T-W-5 Input-output operations and multi threading 2

T-W-6 Windows Forms Applications 2

T-W-7 Exam 1

Student workload - forms of activity Number of hoursLaboratory attendance 30A-L-1

Student individual work 60A-L-2

Lectures attendance 15A-W-1

Student individual work 15A-W-2

Teaching methods / tools

C# Programming Language

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M-2 Discussion

M-3 Work with computers at laboratories

Evaluation methods (F - progressive, P - final)S-1 project workF

S-2 written examP



study





education

Teachingmethods


methods

Knowledge

C-1 S-2T-W-1T-W-2T-W-3

M-1M-2

WM_1-_null_W01After the course the student will know the c# syntax and will beable to define object-oriented programming principles in thecontext of c#

T-W-4T-W-5T-W-6

C-2 S-2T-W-1T-W-2T-W-3

M-1M-2

WM_1-_null_W02After the course the student will be able to explain what ishappening in a c# code.

T-W-4T-W-5T-W-6

Skills

C-1C-2 S-1

T-L-1T-L-2T-L-3T-L-4T-L-5T-L-6

M-2M-3

WM_1-_null_U01The student will be able to write program in a c# language.

T-L-7T-L-8T-L-9T-L-10T-L-11



KnowledgeWM_1-_null_W01 2,0

3,0 The student knows c# syntax.

3,54,04,55,0

WM_1-_null_W02 2,03,0 The student is able to explain code of a simple program written in c#.

3,54,04,55,0

SkillsWM_1-_null_U01 2,0

3,0 The student is able to write a simple program in a c# language.

3,54,04,55,0


Required reading1. John Sharp, Microsoft Visual C# 2012 Step by Step, 20132. Karli Watson, Jacob Vibe Hammer, Jon Reid, Morgan Skinner, Daniel Kemper, Christian Nagel, Beginning Visual C# 2012 Programming,2012

Supplementary reading1. http://en.wikibooks.org/wiki/C_Sharp_Programming

[ logo uczelni ]

Field of study

Mode of study


ECTS


Code

Electives

Language

Elective group


stationary

WI-1-C++

4,0

credits english

ECTS (forms) 4,0

Level first cycle

Area(s) of study


Module

Course unit C++ programming language


Administering faculty Katedra Inżynierii Oprogramowania




Konys Agnieszka ([email protected])Leading teacher

Other teachers


Module/course unit objectivesC-1 Familiar with the syntax, basic programming constructs and principles used in C++ language

C-2 The ability to write small-scale C++ programs using the acquired skills

Course content divided into various forms of instruction Number of hoursT-L-1 Introduction to C++ and IDE 2

T-L-2 Variables, datatypes and operators 3

T-L-3 Input/output operations 3

T-L-4 Conditionals 4

T-L-5 Loops 5

T-L-6 Arrays 4

T-L-7 Structures 3

T-L-8 Functions 4

T-L-9 Input/output with files 2

T-W-1 Introduction to programming and C++ 2

T-W-2 Structure of a program and basic concepts 2

T-W-3 Variables and fundamental data types 3

T-W-4 Input/output operations 3

T-W-5 Constants and operators 3

T-W-6 Conditionals and loops 6

T-W-7 Arrays and multi-dimensional arrays 4

T-W-8 Structures 2

T-W-9 Functions 4

T-W-10 Exam 1





C++ programming language

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M-2 Discussion


Evaluation methods (F - progressive, P - final)S-1 Written examF

S-2 Continuous assessmentF



study





education

Teachingmethods


methods

KnowledgeWM-WI_1-_??_W01After the course the student should be able to understand anduse the basic programming constructs of C++ and write small-scale C++ programs using the above skillsWM-WI_1-_??_W02After the course the student should be able to explain what ishappening in a C++ code

Skills




3,03,54,04,55,0

WM-WI_1-_??_W02 2,03,03,54,04,55,0

Skills


Required reading1. Bjarne Stroustrup, The C++ Programming Language (Fourth Edition), Addison-Wesley, 2012

2. Daoqi Yang, C++ and Object-Oriented Numeric Computing for Scientists and Engineers, Springer, 2001

3. http://www.cplusplus.com/doc/tutorial/

Supplementary reading1. https://en.wikibooks.org/wiki/C%2B%2B_Programming

[ logo uczelni ]

Field of study

Mode of study


ECTS


Code

Electives

Language

Elective group


stationary

WI-1-CPR

4,0

credits english

ECTS (forms) 4,0

Level first cycle

Area(s) of study


Module

Course unit Cloud programming






Radliński Łukasz ([email protected])Leading teacher

Other teachers

Prerequisites

W-1 Basic knowledge and skills in object-oriented programming (preferably in Java, C# and/or Python), databases, webapplications development.

Module/course unit objectivesC-1 Familiarizing with selected cloud platforms.

C-2 Possess knowledge and obtain practical skills in developing cloud-based applications.

C-3 Familiarizing with technologies, techniques and tools for cloud development.

C-4 Practicing individual and team-based work in a software project.

Course content divided into various forms of instruction Number of hoursT-L-1 Introduction to cloud computing. Setting up development environment. Overview of the lab classes. 2

T-L-2 Cloud-based data storage. 2

T-L-3 Security in cloud-based appliations. 2

T-L-4 Analytical and predictive services 2

T-L-5 Multimedia services 2

T-L-6 Other and external services - integration with other providers. 2

T-L-7 Services for mobile devices. 2

T-L-8 Internet of Things. Management tools. 2

T-L-9 DevOps. Deployment and testing cloud-based applications. 2

T-L-10 Developing a student project 10

T-L-11 Project presentations and grading 2

T-W-1 Introduction to cloud computing – features, capabilities and limitations. 1

T-W-2 Cloud computing platforms. Overview of the main services. 1

T-W-3 Cloud-based data storage. 2

T-W-4 Security in cloud-based applications. 2

T-W-5 Analytical and predictive services 2

T-W-6 Multimedia services 2

T-W-7 Other and external services 2

T-W-8 Services for mobile devices. 1

T-W-9 Internet of Things 1

T-W-10 Test for grading 1

Student workload - forms of activity Number of hours

Cloud programming

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Student workload - forms of activity Number of hourspreparing for lab classes 3A-L-1

participation in lab classes 30A-L-2

completing lab exercises at home 33A-L-3

preparing for credits 5A-L-4

consulting during office hours 4A-L-5

participation in lectures 15A-W-1

literature reading 15A-W-2

preparing for credit 13A-W-3

consulting during office hours 2A-W-4

Teaching methods / toolsM-1 Informative lecture with demonstration

M-2 Lab exercises

M-3 Project

Evaluation methods (F - progressive, P - final)S-1 Individual exercisesP

S-2 Individual or group projectP

S-3 Test with open questionsP



study





education

Teachingmethods


methods

Knowledge

C-1C-2C-3

S-3


M-1WM_1-_??_W01Explains core concepts of cloud computing and cloudprogramming.

T-W-6T-W-7T-W-8T-W-9T-W-10

Skills

C-1C-2C-3C-4

S-1S-2


M-1M-2M-3

WM_1-_??_U01Can develop, deploy and manage cloud-based application.

T-L-7T-L-8T-L-9T-L-10T-L-11


C-1C-2C-3C-4

S-1S-2S-3


M-2M-3

WM_1-_??_K01Student has increased awareness and motivation of self-learningof rapidly developing cloud technologies.

T-L-6T-L-7T-L-8T-L-9T-L-10


KnowledgeWM_1-_??_W01 2,0

3,0 Student can explain and distinguish majority of core concepts of cloud computing and cloud programming on a singleplatform.

3,54,04,55,0

SkillsWM_1-_??_U01 2,0

3,0 Student can develop, deploy and manage a simple cloud-based application on a specific single platform.

3,54,04,55,0

[ logo uczelni ]

Other social / personal competencesWM_1-_??_K01 2,0

3,0 Student can gain technical knowledge on cloud technologies with self learning.

3,54,04,55,0

Required reading1. Erl T., Puttini R., Mahmood Z., Cloud Computing: Concepts, Technology & Architecture, Prentice Hall, 2013

2. IBM Cloud Docs, https://bluemix.net/docs/

3. AWS Documentation, https://aws.amazon.com/documentation

4. Google Cloud Documentation, https://cloud.google.com/docs/

Supplementary reading1. Redkar T., Windows Azure Web Sites: Building Web Apps at a Rapid Pace, CreateSpace Independent Publishing Platform, 2013

2. Rhoton J., Cloud Computing Explained: Implementation Handbook for Enterprises, Recursive Press, 2010, 2

3. Sanderson D., Programming Google App Engine, O'Reilly Media, 2012, 2

[ logo uczelni ]

Field of study

Mode of study


ECTS


Code

Electives

Language

Elective group


stationary

WI-1-COM

6,0

credits english

ECTS (forms) 6,0

Level first cycle

Area(s) of study


Module

Course unit Compilers






Bielecki Włodzimierz ([email protected])Leading teacher

Other teachers

PrerequisitesW-1 You are expected to have some basic programming skills using C, or C++ or java.


C-1To be able to: build lexical analyzers and use them in the construction of parsers; express the grammar of a programminglanguage; build syntax analyzers and use them in the construction of parsers; perform the operations of semantic analysis;build a code generator; discuss the merits of different optimization schemes.

Course content divided into various forms of instruction Number of hoursT-L-1 Define the simple computer architecture and programming language of this computer 4

T-L-2 Implementation of a lexical analyzer for a defined programming language using the FLEX tool 4

T-L-3 Implementation of the parser for the defined language using the BISON tool 3

T-L-4 Implementation of defined semantic actions 4

T-L-5 Implementation of the code generator for arithmetic expressions for the defined computer architecture 3

T-L-6 Code generation for conditional statements and loops 3

T-L-7 Implementation of the use of single- and multi-dimensional tables 3

T-L-8 Implementation of the code generator for various data types 3

T-L-9 Implementation of the code generator for various data types 3 3

T-W-1 Compiler structure 2

T-W-2 Lexical analysis 4

T-W-3 Top down parsing 4

T-W-4 Bottom up parsing 4

T-W-5 Lex and Yacc 4

T-W-6 Semantic analysis 2

T-W-7 Code generation, SPIM 4

T-W-8 A simple translator 4

T-W-9 Implementation of function calls 2

Student workload - forms of activity Number of hoursparticipation in laboratories 30A-L-1

preparation for laboratories 50A-L-2

Participation in consultations 10A-L-3

Lectures 30A-W-1

Preparing to examination 50A-W-2

Examination 2A-W-3

Compilers

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Student workload - forms of activity Number of hoursconsultations 8A-W-4

Teaching methods / toolsM-1 Informative / conversational lectures

M-2 Laboratory exercises

Evaluation methods (F - progressive, P - final)S-1 Assessment of the degree of practical tasks at the end of each laboratoryF

S-2 the Final exam by checking the learning outcomes: presenting questions and assessing the answersF



study





education

Teachingmethods


methods

KnowledgeWM-WI_1-_??_W01The student has basic knowledge in the field of compilerdesign

SkillsWM-WI_1-_??_U01The student is able to design a simple compiler.

Other social / personal competencesWM-WI_1-_??_K01The student is able to work with colleagues in a group.



3,03,54,04,55,0


3,03,54,04,55,0


3,03,54,04,55,0

Required reading1. A.V. Aho, R. Sethi and J.D. Ullman, Compilers - Principles, Techniques, and Tools', Addison-Wesley, Boston, 2007

[ logo uczelni ]

Field of study

Mode of study


ECTS


Code

Electives

Language

Elective group


stationary

WI-1-CPH

3,0

credits english

ECTS (forms) 3,0

Level first cycle

Area(s) of study


Module

Course unit Computational photography








PrerequisitesW-1 Programming skills in a scripting language (e.g. Matlab).

Module/course unit objectivesC-1 Gaining basic knowledge, skills, and competences in computational photography.


T-P-1 Implementation of a project involving the implementation of the program (in a script language likeMatlab or Python) and acquisition of a HDR image based on this software. 30

T-W-1 Hardware in digital photography 2

T-W-2 Physical aspect of digital photography 2

T-W-3 High dynamic range (HDR) photography 2

T-W-4 Color profiles and color correction 2

T-W-5 Tone mapping and visualization 3

T-W-6 HDR image acquisition 4

Student workload - forms of activity Number of hoursParticipation in project workshops. 30A-P-1

Implementation of software as part of homework. 30A-P-2

Participation in lectures 15A-W-1

Learning to pass the subject 15A-W-2


M-2 workshop

Evaluation methods (F - progressive, P - final)S-1 Finished projectP



study





education

Teachingmethods


methods

Knowledge

C-1 S-1M-1WM-WI_1-_??_W01Gaining basic knowledge on computational photography.

Skills

C-1 S-1M-2WM-WI_1-_??_U01Gaining basic skills in computational photography.

Computational photography

[ logo uczelni ]


C-1 S-1M-2WM-WI_1-_??_K01Gaining basic competences in computational photography.



3,03,54,04,55,0


3,03,54,04,55,0


3,03,54,04,55,0

Required reading1. Rastislav Lukac, Computational Photography: Methods and Applications, CRC Press, 2016, 1

[ logo uczelni ]

Field of study

Mode of study


ECTS


Code

Electives

Language

Elective group


stationary

WI-1-CTN

4,0

credits english

ECTS (forms) 4,0

Level first cycle

Area(s) of study


Module

Course unit Computer and telecommunication networks







Other teachers

PrerequisitesW-1 Basics of programming; Architecture of computer systems; Operating systems fundamentals.

Module/course unit objectivesC-1 Knowledge of reference models, network standards, protocols of data link layer, network, transport and application layers.

C-2 Knowledge of current wired and wireless network solutions.

C-3 Ability of network’s performance evaluation.

C-4 Ability of simple home/office network building.

C-5 Basic algorithms of data link, network and application layer implementation ability.

Course content divided into various forms of instruction Number of hoursT-L-1 Implementation of the program implementing the CRC algorithm. 8

T-L-2 Implementation of the program implementing the routing algorithm selected. 8

T-L-3 Implementation of the program implementing selected network application (eg. chat, file transfer, etc.) 8

T-L-4 Introduction to simulation of computer networks. Building of a simulation model for a simple network. 6

T-W-1 Introduction to computer networks. 2

T-W-2 Physical layer, transmission media, multiplexing techniques, circuit and packet switching. 4

T-W-3 Data link layer, error detection, flow control, ALOHA and CSMA protocols, protocols without collisions,Ethernet, wireless local area networks, interconnecting. 6

T-W-4 Network layer, routing algorithms and protocols, quality of service, Internet Protocol. 6

T-W-5 Transport layer, protocols, addressing, flow control, UDP, TCP and RTP protocols, Nagle’s and Clarke’salgorithms. 6

T-W-6 Application layer, DNS, e-mail, WWW, multimedia applications of the networks. 6





Attendance in the classes 30A-W-1

Preparation for the exam 25A-W-2

Exam 3A-W-3

Consultations to the lecture 2A-W-4

Teaching methods / toolsM-1 Lecture with presentation

Computer and telecommunication networks

[ logo uczelni ]

Teaching methods / toolsM-2 Laboratory work

Evaluation methods (F - progressive, P - final)S-1 Lecture - written examP

S-2 Laboratory work - written reportsF

S-3 Laboratory work - evaluation of submitted programs and projectP



study





education

Teachingmethods


methods

Knowledge

C-1C-2 S-1

T-W-1T-W-2T-W-3

M-1WM_1-_null_W01Student will gain detailed knowledge of network technologies

T-W-4T-W-5T-W-6

Skills

C-3 S-2S-3

T-L-4M-2

WM_1-_null_U01Student is capable of running simulation package specialized incomputer networks

C-4C-5

S-2S-3

T-L-1T-L-2 M-2

WM_1-_null_U02Student is able to prepare programs implementing selectednetworking aspects

T-L-3




3,0 Student knows the network layers, can name basic communication protocols, is also familiar with the fundamentals of IPaddressing, network topologies and network technologies that are currently used.

3,54,04,55,0


3,0 Basic ability of using simulation package (Riverbed Modeler) - loading of prepared design, simulation, gathering of theresults.

3,54,04,55,0

WM_1-_null_U02 2,03,0 Basic skills in the implementation of selected networking aspects.

3,54,04,55,0


Required reading1. A. S. Tanenbaum, Sieci komputerowe, Helion, Gliwice, 2004

2. M. Hassan, R. Jain, Wysoko wydajne sieci TCP/IP, Helion, Gliwice, 2004

[ logo uczelni ]

Field of study

Mode of study


ECTS


Code

Electives

Language

Elective group


stationary

WI-1-CGP

4,0

credits english

ECTS (forms) 4,0

Level first cycle

Area(s) of study


Module

Course unit Computer Games Programming








PrerequisitesW-1 Programming skills in C/C++ languages.

Module/course unit objectivesC-1 Gaining knowledge, skills, and competences on the computer games programming.

Course content divided into various forms of instruction Number of hoursT-P-1 Implementation of a project involving the implementation of the basic computer game. 60

T-W-1 Introduction to graphic libraries. 2

T-W-2 Geometric transformations. 1

T-W-3 User interface and time synchronisation. 2

T-W-4 Game loop architecture. 2

T-W-5 Aggregated game board. 1

T-W-6 Collision detection. 2

T-W-7 Lights and materials. 2

T-W-8 Materials and texture. 3

Student workload - forms of activity Number of hoursParticipations in workshops. 60A-P-1

Implementation of game as part of homework. 30A-P-2



Teaching methods / toolsM-1 Lectures

M-2 Workshops

Evaluation methods (F - progressive, P - final)S-1 Finished project (impemented computer game).P



study





education

Teachingmethods


methods

Knowledge

C-1 S-1T-W-1T-W-2T-W-3T-W-4

M-1WM-WI_1-_??_W01Gaining knowledge on computer games programming.

T-W-5T-W-6T-W-7T-W-8

Computer Games Programming

[ logo uczelni ]

Skills

C-1 S-1T-P-1 M-2WM-WI_1-_??_U01Gaining skills in computer games programming.


C-1 S-1T-P-1 M-2WM-WI_1-_??_K01Gaining competences in computer games programming.



3,0 Finished implementation of the game.

3,54,04,55,0



3,54,04,55,0



3,54,04,55,0

Required reading1. Michael Dawson, Beginning C++ Through Game Programming, Cengage Learning PTR, 2010, 3

[ logo uczelni ]

Field of study

Mode of study


ECTS


Code

Electives

Language

Elective group


stationary

WI-1-CSI

6,0

credits english

ECTS (forms) 6,0

Level first cycle

Area(s) of study


Module

Course unit Computer modelling and simulation






Korytkowski Przemysław ([email protected])Leading teacher

Korytkowski Przemysław ([email protected])Other teachers

PrerequisitesW-1 Basic computer techniques, particularly basic file management under Windows, Excel and word processing.

W-2 Basic statistics concepts, probability and stochastic processes, particularly the exponential, normal and uniformdistributions.


C-1 Be able to simulation modeling using a computer; Understand the assumptions, strengths and weaknesses of simulationmodels; Analyze and translate problems into a form suitable for applying simulation strategies; Validate a simulation model.

Course content divided into various forms of instruction Number of hoursT-L-1 Modeling and estimating input processes 4

T-L-2 Modeling basics operations and inputs 4

T-L-3 Statystical analysis of output 4

T-L-4 Transport modeling 4

T-L-5 Symulation analysis project 14

T-W-1 Introduction to modelling 2

T-W-2 Fundamental Simulation Concepts 2

T-W-3 Introduction and overview of simulation analysis 2

T-W-4 Modeling and estimating input processes 2

T-W-5 Statistical analysis of simulation output 2

T-W-6 Comparison, ranking, and selection of simulation models 2

T-W-7 Design of experiment 3


Project 60A-L-2


Homeworks and self study 45A-W-2

Teaching methods / toolsM-1 Lectures with case studies

M-2 Projects

Evaluation methods (F - progressive, P - final)S-1 Project report & presentationP

S-2 Case studies and small projectsF

Computer simulation

[ logo uczelni ]



study





education

Teachingmethods


methods

Knowledge

C-1 S-1T-W-1T-W-2

WM_1-_null_W02Understand the assumptions, strengths and weaknesses ofsimulation models;

T-W-3

Skills

C-1 S-1T-L-2T-L-3T-L-4T-L-5

WM_1-_null_U01Analyze and translate problems into a form suitable for applyingsimulation strategies





3,0 Student understands basic terms and notions.

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SkillsWM_1-_null_U01 2,0 Student is not able to model and analyse a simple system.

3,0 Student is able to model and analyse a simple system.

3,5 Student is able to model and analyse simple system with input data analysis.

4,0 Student is able to model and analyse simple system with input and output data analysis.

4,5 Student is able to model and analyse complex system with input and output data analysis.

5,0 Student is able to model and analyse complex system with input, output data analysis and prepare a proper designg ofexperiment.


Required reading1. W. David Kelton, Randall P. Sadowski, and David T. Sturrock, Simulation with Arena, McGraw Hill, New York, 2004, 3

2. J. Banks, J. S. Carson B. L. Nelson, and D. M. Nicol, Discrete-Event System Simulation, Prentice Hall, New York, 2005

Supplementary reading1. Law, A. M. & Kelton, W. D., Simulation Modelling & Analysis, McGraw Hill, Boston, 2000

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Field of study

Mode of study


ECTS


Code

Electives

Language

Elective group


stationary

WI-1-CSA

4,0

credits english

ECTS (forms) 4,0

Level first cycle

Area(s) of study


Module

Course unit Computer System Architecture






Kapruziak Mariusz ([email protected])Leading teacher

Other teachers

Prerequisites

W-1 Digital design.Basics of Electronics.

Module/course unit objectivesC-1 Processor programming on different architectures.

C-2 Knowledge of history and concepts of current processor and computer design.

Course content divided into various forms of instruction Number of hoursT-L-1 PC Mainboard. 2

T-L-2 Assembler language for x86 processor - native program. 4

T-L-3 Assembler for x86 - stack and mixing C and assembler. 4

T-L-4 Communication port programming (Visual Studio). 2

T-L-5 Sound card programming. 4

T-L-6 Camera programming. 2

T-L-7 Robot control on PC (programming). 2

T-L-8 ARM processor programming 2

T-L-9 FPGA programming (as an alternative to von Neumann processor). 2

T-L-10 Project. 6

T-L-11 SSE and vector units. 2

T-W-1 Von Neumann machine and history of computer architectures. 2

T-W-2 Execution and control unit functionality (on example of x86 and PIC architecture). 5

T-W-3 Memory hierarchy and cache memory (its influence on efforts on program code optimization inparticular) 2

T-W-4 ARM architecture and low power designs (like palmtops, smartphones) 4

T-W-5 Protected mode and its influence on modern operation systems, driver design for MS Windows andLinux systems 2

T-W-6 Instruction Level Paralellism (especially superscalar and VLIW/DSP architectures) 4

T-W-7 Modern PC microprocessors 5

T-W-8 Supercomputers and networks of computers aimed to solve particular problems 2

T-W-9 Reconfigurable systems and modern alternatives to von Neumann machines. 4

Student workload - forms of activity Number of hoursLaboratories. 24A-L-1

Project 6A-L-2

Computer System Architecture

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Student workload - forms of activity Number of hoursIndividual work 30A-L-3

Lectures 30A-W-1

Individual work 30A-W-2

Teaching methods / toolsM-1 Lectures

M-2 Laboratories

M-3 Project

Evaluation methods (F - progressive, P - final)S-1 Laboratories project.F

S-2 Laboratory raports.F

S-3 Exam.P



study





education

Teachingmethods


methods

KnowledgeWM-WI_1-_??_W01Student knows fundamental processor structures and candescribe them.

SkillsWM-WI_1-_??_U01Student can programm basic codes in the assembler language.WM-WI_1-_??_U02Student can program code for basic peripheral devices.




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WM-WI_1-_??_U02 2,03,03,54,04,55,0


Required reading1. W. Stallings, Computer Organization and Architecture, Prentice Hall, 2003

2. J. Stokes, Inside the Machine, No Starch Press

3. J. Silc, B. Robic, T Ungerer, Processor Architecture From Dataflow to Superscalar and Beyond, Springer Verlag, 1999

4. K. Kaspersky, Code Optimization: Effective Memory Usage, A-List Publishing

Supplementary reading1. W. Oney, Programming the Microsoft Windows Driver Model, Microsoft Press

2. P. Raghavan, A. Lad, S. Neelakandan, Embedded Linux System Design and Development, Auerbach Publications

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Field of study

Mode of study


ECTS


Code

Electives

Language

Elective group


stationary

WI-1-CVS

3,0

credits english

ECTS (forms) 3,0

Level first cycle

Area(s) of study


Module

Course unit Computer Vision for Video Surveillance






Nowosielski Adam ([email protected])Leading teacher

Other teachers

PrerequisitesW-1 Elementary digital image processing

W-2 Elementary numerical recipes

W-3 Elementary programming skills

W-4 Elementary matrix algebra


C-1 The main objective of the course is to familiarize students with the range of possibilities and principles of the modernintelligent monitoring systems.

C-2 Students will be prepared to design intelligent surveillance system performing the tasks of automatic processing, analysisand recognition of digital images.

Course content divided into various forms of instruction Number of hoursT-L-1 Introduction to laboratory classes. 1

T-L-2 Video surveillance at the Faculty and on the campus. The ALPR system. Image acquisition fromcameras. 2

T-L-3 Performance verification of available (ready to use, implemented) algorithms for video surveillance,e.g.: background modelling, object detection, object recognition, object tracking 4

T-L-4 Implementation of selected algorithms for video surveillance, e.g.: background modelling, objectdetection, object recognition, object tracking. 4

T-L-5 Development of a concept of simple video surveillance system. Definition of the scope of the project.Design and implementation of own simple video surveillance system. 4

T-W-1Introduction to video surveillance systems. Selected issues and classification of monitoring systems.Legal regulations. Systems of video-observation. Hardware in video monitoring systems. IntelligentBuilding. Intelligent cameras. Mobile wireless platforms. Access control controllers.

3

T-W-2 Thermal imaging for video observation. 1

T-W-3 Intelligent Transport Systems (ITS): ALPR, WIM, HIM, red-light, others. Measuring traffic congestion.Intelligent parking. 2

T-W-4 Background modeling methods. 2

T-W-5 Autoamtic detection and recognition of objects in video surveilance systems. 3

T-W-6 Tracking algorithms. 1

T-W-7 Example implementations of intelligent video surveillance systems: vehicle traffic measurementsystems, human traffic analysis, people identification based on biometric features, etc. 3

Student workload - forms of activity Number of hoursParticipation in classes. 15A-L-1

Preparation for the classes. 7A-L-2

Participation in the consultations for laboratories. 4A-L-3

Preparation of reports for selected laboratories. 8A-L-4

Computer Vision for Video Surveillance

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Student workload - forms of activity Number of hoursDesign and implementation of own video surveillance system. 19A-L-5

Preparation of the final report. 7A-L-6

Presence at lectures. 15A-W-1

Literature reading. 15A-W-2

Teaching methods / toolsM-1 Lectures: informative, problem solving, conversational

M-2 Laboratory classes with a computer

M-3 Problems discution at laboratory classes

M-4 Discussion of the individual project, brainstorm

Evaluation methods (F - progressive, P - final)S-1 Assessment of the project created during practical exercises and discussion of the final repot.P

S-2 Presentation and defense of the project in front of a group of students.F

S-3 Progress monitoring in implementation of own video surveillance system.F

S-4 Verification of reports from selected laboratories.F



study





education

Teachingmethods


methods

KnowledgeWM-WI_1-_??_W01Students are familiarized with the computer vision methodsapplicable to video surveillance. Students are acquainted withprinciples of the modern intelligent monitoring systems.

SkillsWM-WI_1-_??_U01Students are prepared to design intelligent surveillance systemperforming the tasks of automatic processing, analysis andrecognition of digital images.

Other social / personal competencesWM-WI_1-_??_K01The student is aware of the role of video surveillance systemsfor the society.



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Required reading1. H. Kruegle, CCTV Surveillance, Second Edition: Video Practices and Technology, Butterworth-Heinemann, 2006, 672 p.

2. R. Gonzalez, R. Woods, S. L. Eddins, Digital Image Processing Using MATLAB 2nd Ed., Gatesmark Publishing, 2009, 827 p.

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Supplementary reading1. J. S. Sussman, Perspectives on Intelligent Transportation Systems (ITS), Springer, 2005, 229 p.

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Field of study

Mode of study


ECTS


Code

Electives

Language

Elective group


stationary

WI-1-DAM

3,0

credits english

ECTS (forms) 3,0

Level first cycle

Area(s) of study


Module

Course unit Data Analysis and Machine Learning






Klęsk Przemysław ([email protected])Leading teacher

Other teachers

PrerequisitesW-1 mathematics

W-2 algorithms and data structures

W-3 programming

W-4 probability calculus and statistics

Module/course unit objectivesC-1 Developping a general understanding about data analysis and machine learning methods.

Course content divided into various forms of instruction Number of hoursT-L-1 Programming PCA in MATLAB. 3

T-L-2 Programming CART trees in MATLAB. 4

T-L-3 Programming SVM optimization tasks (several versions) in MATLAB. 4

T-L-4 Programming MARS algorithm in MATLAB. 4

T-W-1Principal Component Analysis (PCA) as a method for dimensionality reduction. Review of notions:variance, covariance, correlation coefficient, covariance matrix. Minimization of projection lengths ofdata points onto a given direction. Derivation of PCA. Interpretation of eigenvalues and eigenvectors.

3

T-W-2 Decision trees - CART algorithm. Impurity functions, greedy generation of a complete tree. Pruningheuristics for decision trees (depth-based, leaves-based). 3

T-W-3Support Vector Machines (SVM). Distance of data points from the decision hyperplane. Separationmargin. Formulation of the SVM optimization task without and with Lagrange multipliers. Supportvectors - what are they? Soft-margin SVM and related optimization tasks. SVMs with non-linear decisionboundary using the kernel trick.

5

T-W-4Multivariate Adaptive Regression Splines (MARS) for approximation tasks. Construction of splines.Least-squares approximation with arbitrary bases (in particular MARS splines). Learning algorithm.Similarities to CART.

2

T-W-5 Exam. 2

Student workload - forms of activity Number of hoursParticipation in lab classes. 15A-L-1

Programming homework assignments. 40A-L-2

Preparation for short tests conducted in the lab at the end of each topic. 4A-L-3


Preparation for the exam. 15A-W-2

Sitting for the exam. 2A-W-3

Teaching methods / toolsM-1 Lecture.

Data Analysis and Machine Learning

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Teaching methods / toolsM-2 Computer programming.

Evaluation methods (F - progressive, P - final)S-1 Four short tests (15 minutes long) at the end of each topic during the lab.F

S-2 Four grades for the programs written as homeworks.F

S-3Final grade for the lab calculated as a weighted mean from partial grades:- tests (weight: 40%),- programs (weight: 60%).

P

S-4 Final grade for lectures from the test (2 h).P



study





education

Teachingmethods


methods

KnowledgeWM-WI_1-_??_W01Student posesses an elementary knowledge on machinelearning algorithms and techniques of data analysis.

SkillsWM-WI_1-_??_U01Student can implement (in Python or MATLAB) several machinelearning algorithms and techniques.




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Required reading1. M. J. Zaki, W. Meira Jr, Data Mining and Analysis - Fundamental Concepts and Algorithms, Cambridge University Press, 2014

2. P. Klęsk, Electronic materials for the course available at: http://wikizmsi.zut.edu.pl, 2015

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Field of study

Mode of study


ECTS


Code

Electives

Language

Elective group


stationary

WI-1-DSY

6,0

credits english

ECTS (forms) 6,0

Level first cycle

Area(s) of study


Module

Course unit Database systems







Other teachers

Prerequisites


C-1 Design of databasesSQL language proficiency

Course content divided into various forms of instruction Number of hoursT-L-1 ERD diagrams 4

T-L-2 SQL 20

T-L-3 DBMS management 6

T-W-1 Worlds of database systems 2

T-W-2 Relational Database Systems: SQL 24

T-W-3 SQL in server envinronment 4


Homework 90A-L-2


Homework 30A-W-2


Evaluation methods (F - progressive, P - final)S-1 Written examP



study





education

Teachingmethods


methods

KnowledgeWM-WI_1-_??_W01Student is able to describe various types of databases.

SkillsWM-WI_1-_??_U01Student is able to design a database. Student is able to freelycreate SQL code.


Database systems

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Required reading1. Garcia-Molina, Ullman, Widom, Database Systems. The complete book, Pearson, Upper Saddle River, 2009

[ logo uczelni ]

Field of study

Mode of study


ECTS


Code

Electives

Language

Elective group


stationary

WI-1-DMA

4,0

credits english

ECTS (forms) 4,0

Level first cycle

Area(s) of study


Module

Course unit Data Mining Algorithms







Other teachers


W-2 programming


Module/course unit objectivesC-1 Building the understanding about learning from data.

C-2 Familiarization with probabilistic, tree-based, and boosted classifiers, and the related algorithms.

C-3 Familiarization with rules mining and related algorithms.


T-L-1 Programming the naive Bayes classifier (MATLAB) - for 'wine data set' (in class) and a selected data set(homework). 8

T-L-2 Programming the Apriori algorithm - mining association rules. 6

T-L-3 Programming an exhaustive generator of decision rules (for given premise length). 6

T-L-4 Programming the CART algorithm - building a complete tree. 4

T-L-5 Programming heuristics for pruning CART trees. 6

T-W-1Review of some elements of probability calculus. Derivation of Naive Bayes classifier. Remarks oncomputational complexity with and without the naive assumption. Bayes rule. LaPlace correction. Betadistributions.

4

T-W-2Mining association rules by means of Apriori algorithm. Support and confidence measures. Findingfrequent sets (induction). Rules generation mechanics. Remarks on the hashmap data structureapplied for Apriori algorithm. Pareto-optimal rules. Remarks on decision rules generation.

4

T-W-3Decision trees and CART algorithm. Impurity functions and their properties. Best splits as minimizers ofexpected impurity of children nodes. CART greedy algorithm. Tree pruning heuristics (by depth, bypenalizing number of leafs). Recursions for traversing the subtrees (greedy and exhaustive).

3

T-W-4 Ensemble methods: bagging and boosting (meta classifiers). AdaBoost algorithm. Exponential criterionvs zero-one-loss function. Real boost algorithm. 2

T-W-5 Exam. 2


Programming homework tasks. 40A-L-2

Preparation for short tests (15 min) carried out in lab classes. 15A-L-3




Data Mining Algorithms

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Teaching methods / toolsM-1 Lectures.

M-2 Computer programming.




P




study





education

Teachingmethods


methods

KnowledgeWM-WI_1-_??_W01Student has an elementary knowledge on data miningalgorithms and notions.

SkillsWM-WI_1-_??_U01Student can implement (MATLAB or Python) data miningalgorithms presented during lectures.




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Required reading1. M. J. Zaki, W. Meira Jr, "Data Mining and Analysis - Fundamental Concepts and Algorithms", Cambridge University Press, 2014


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Field of study

Mode of study


ECTS


Code

Electives

Language

Elective group


stationary

WI-1-DWB

5,0

credits english

ECTS (forms) 5,0

Level first cycle

Area(s) of study


Module

Course unit Data Warehousing and Big Data






Różewski Przemysław ([email protected])Leading teacher

Other teachers

PrerequisitesW-1 SQL basics, basic understanding of main business processes

Module/course unit objectivesC-1 Be able to design Data Warehouse and use MDX effectively.

Course content divided into various forms of instruction Number of hoursT-L-1 Conceptual and Logical Data Warehouse Design 4

T-L-2 ETL process design 6

T-L-3 SQL Server Integration Services 6

T-L-4 SQL Server Analysis Services (SSAS) 12

T-L-5 Power BI 2

T-W-1 Data Warehouse Concepts 3

T-W-2 Conceptual and Logical Data Warehouse Design 4

T-W-3 Querying Data Warehouses (MDX) 6

T-W-4 Extraction, Transformation, and Loading (ETL) 3

T-W-5 Working with Big Data 4

T-W-6 Integration of Big Data and Data Warehousing 8

T-W-7 New Data Warehouse Technologies (Spatial, Trajectory, Semantic Web) 2


Homework 45A-L-2


Homework 45A-W-2





study





education

Teachingmethods


methods

Knowledge

Data Warehousing and Big Data

[ logo uczelni ]

WM-WI_1-_??_W01Student will know how to integrate the Big Data and DataWarehousing.

SkillsWM-WI_1-_??_U01Student is able to design and querying Data Warehouse.




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3,03,54,04,55,0


Required reading1. Alejandro Vaisman Esteban Zimányi, Data Warehouse Systems Design and Implementation, Springer-Verlag Berlin Heidelberg, 2013,DOI: 10.1007/978-3-642-54655-6

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Field of study

Mode of study


ECTS


Code

Electives

Language

Elective group


stationary

WI-1-DLV

5,0

credits english

ECTS (forms) 5,0

Level first cycle

Area(s) of study


Module

Course unit Deep learning for visual computing









PrerequisitesW-1 Programming skills in a scripting language (Phyton).

Module/course unit objectivesC-1 Gaining knowledge, skills, and competences about CNNs for visual computing

Course content divided into various forms of instruction Number of hoursT-L-1 Introduction to CNN toolkit. 2

T-L-2 Input data acquisition task. 6

T-L-3 Learning and validation of CNN. 4

T-L-4 Cross-validation example. 2

T-L-5 Calibration of the network. 1

T-P-1 Implementation of a project involving the acquisition of input data and learning CNN for identificationof objects in images. 30

T-W-1 Introduction to convolutional neural networks (CNN). 3

T-W-2 CNN toolkits. 2

T-W-3 Input data acquisition. 3

T-W-4 Tutorial: solving basic object classification problem. 5

T-W-5 Learning CNN with cross-validation. 2

Student workload - forms of activity Number of hoursParticipation in workshops. 15A-L-1

Input data acquisition as a part of homework. 45A-L-2

Participation in workshops. 30A-P-1

Network calibrationas a part of homework. 30A-P-2


Learningto pass the exam. 15A-W-2


M-2 Workshops

Evaluation methods (F - progressive, P - final)S-1 Finished project on detection task using CNN.P

Deep learning for visual computing

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study





education

Teachingmethods


methods

Knowledge

C-1 S-1


M-1WM-WI_1-_??_W01Gaining basic knowledge on CNNs for visual computing.


Skills

C-1 S-1T-P-1 M-1M-2

WM-WI_1-_??_U01Gaining skills on training CNNs.


C-1 S-1T-P-1 M-1M-2

WM-WI_1-_??_K01Gaining competence in training CNNs.



3,0 Finished project

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Required reading1. Ragav Venkatesan, Baoxin Li, Convolutional Neural Networks in Visual Computing: A Concise Guide, CRC Press, 2017

[ logo uczelni ]

Field of study

Mode of study


ECTS


Code

Electives

Language

Elective group


stationary

WI-1-DCI

3,0

credits english

ECTS (forms) 3,0

Level first cycle

Area(s) of study


Module

Course unit Digital Circuits







Other teachers

PrerequisitesW-1 Boolean algebra fundamentals

Module/course unit objectivesC-1 Practical skills in basic digital circuits modeling using VHDL

Course content divided into various forms of instruction Number of hoursT-L-1 Introduction to programable devices and software tools 2

T-L-2 Digital logic gates and boolean functions simplicfication 2

T-L-3 Combinatorial circuits design 2

T-L-4 Flip-flops principle of operation 2

T-L-5 Sequential circuits design 4

T-L-6 Designing a fully functional digital system 3

T-W-1 Hardware design modeling. Reprogrammable devices. Hardware descriprion languages. Introduction toVHDL. 2

T-W-2 Base VHDL syntax. Simulation and synthesis constructs. 2

T-W-3 Combinatorial logic 4

T-W-4 Sequential logic 4

T-W-5 Design at register transfer level 2

T-W-6 Exam 1

Student workload - forms of activity Number of hoursAttendance in classes 15A-L-1

Reports preparation 30A-L-2

Self study 15A-L-3

Attendance in lectures 14A-W-1

Self study 15A-W-2

Exam 1A-W-3

Teaching methods / toolsM-1 Lecture with presentations

M-2 Self-performed laboratory tasks


S-2 Reports evaluationF

Digital Circuits

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study





education

Teachingmethods


methods

Knowledge

C-1 S-1T-W-1T-W-2T-W-3 M-1

WM-WI_1-_??_W01The student knows the structure and rules of operation of basicdigital circuits: logical and sequential, knows the principles ofsimple design circuits using hardware description language.

T-W-4T-W-5

Skills

C-1 S-2T-L-1T-L-2T-L-3 M-2

WM-WI_1-_??_U01The student can builld basic digital circuits: logical andsequential, and implement simple circuits using hardwaredescription language.

T-L-4T-L-5T-L-6




3,0 The student knows basic digital components, knows classical and HDL based design methods

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3,0 The student design basic digital components using classic and HDL based design methods and tools.

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Required reading1. Mano M.M.R., Kime Ch.R., Martin T., Logic & Computer Design Fundamentals, 5th Edition, Pearson, 2016

2. Mano M.M.R, Ciletti M.D., Digital Design: With an Introduction to the Verilog HDL, VHDL, and SystemVerilog, Pearson, 2018, 6

[ logo uczelni ]

Field of study

Mode of study


ECTS


Code

Electives

Language

Elective group


stationary

WI-1-DCM

6,0

credits english

ECTS (forms) 6,0

Level first cycle

Area(s) of study


Module

Course unit Digital color management








Prerequisites


C-1

Upon successful completion of the course, the student will be able to:• Describe colour phenomena• Apply various colour spaces (CIE LAB, CIE XYZ, CIE xyY, CIE LUV, RGB, CMYK)• Measure colour parameters using spectrophotometer• Use ICC profiles in a color workwlow• Organize a reliable colour management system

Course content divided into various forms of instruction Number of hoursT-L-1 Monitor and projector calibration 6

T-L-2 Input device calibration 6

T-L-3 Printer calibration 18

T-W-1 Human colour reception 4

T-W-2 Standard colour spaces 2

T-W-3 Colour measurement 2

T-W-4 ICC profiles 2

T-W-5 Devices calibration 3

T-W-6 Colour Management System 2

Student workload - forms of activity Number of hoursparticipation in class 30A-L-1

Homeworks 90A-L-2

participation in class 30A-W-1

self study 30A-W-2

Teaching methods / toolsM-1 Wykład informacyjny

Evaluation methods (F - progressive, P - final)S-1 Zaliczenie ustneP



study





education

Teachingmethods


methods

Knowledge

Digital color management

[ logo uczelni ]

WM-WI_1-_??_W01Upon successful completion of the course, the student will beable to:• Describe colour phenomena• Describe various colour spaces (CIE LAB, CIE XYZ, CIE xyY, CIELUV, RGB, CMYK)

SkillsWM-WI_1-_??_U01Upon successful completion of the course, the student will beable to:• Apply various colour spaces (CIE LAB, CIE XYZ, CIE xyY, CIELUV, RGB, CMYK)• Measure colour parameters using spectrophotometer• Use ICC profiles in a color workwlow• Organize a reliable colour management system




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Required reading1. Fraser, B., C. Murphy, F. Bunting, Real World Color Management, Peachpit Press, 2004

2. Sharma, A., Understanding Color Management, Delmar Cengage Learning, 2003

Supplementary reading1. Giorgianni, E.J., T.E. Madden, M.A. Kriss, Digital Color Management: Encoding Solutions, Wiley, 2009

[ logo uczelni ]

Field of study

Mode of study


ECTS


Code

Electives

Language

Elective group


stationary

WI-1-DDO

3,0

credits english

ECTS (forms) 3,0

Level first cycle

Area(s) of study


Module

Course unit Dynamic documents and front-end Web development






Jankowski Jarosław ([email protected])Leading teacher

Other teachers

PrerequisitesW-1 The basics of HTML language

Module/course unit objectivesC-1 Understanding selected programming languages and data processing methods in dynamic Web systems.

Course content divided into various forms of instruction Number of hoursT-L-1 Dynamic access to web page elements in object-oriented document model 2

T-L-2 Dynamic modification of Web content 2

T-L-3 Formatting content using CSS sheets 4

T-L-4 Capturing events using Java Script 2

T-L-5 Construction of validators and forms 4

T-L-6 Encoding data using XML language 4

T-L-7 Integration of selected components and construction of asynchronous applications 6

T-L-8 Use of selected libraries in dynamic document programming 6

T-W-1 Document object model 2

T-W-2 CSS sheets 2

T-W-3 Application of Java Script in dynamic documents 2

T-W-4 XML markup language 2

T-W-5 AJAX and asynchronous processing 4

T-W-6 Selected applications and libraries integrating these technologies 3

Student workload - forms of activity Number of hoursParticipation in didactic classes 30A-L-1

Participation for assessment 4A-L-2

Preparation of laboratory reports 26A-L-3

Participation in didactic classes 15A-W-1

Participation in consultations and examinations 4A-W-2

Preparing for the exam 12A-W-3

Teaching methods / toolsM-1 Lecture with presentations and examples

M-2 Practical exercises in laboratories


Dynamic documents and front-end Web development

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S-1 Lecture - Written examin with practical questions, questions in the form of a selection and description - a total of 10questionsP

S-2 Overall assessment based on reports and attendanceP



study





education

Teachingmethods


methods

Knowledge

C-1 S-1S-2

T-L-1T-L-2T-L-3T-L-4T-L-5T-L-6T-L-7

M-1M-2

WM-WI_1-_null_W01Wiedza w zakresie programowania dokumentów dynamicznychw systemach internetowych

T-L-8T-W-1T-W-2T-W-3T-W-4T-W-5T-W-6

Skills

C-1 S-1S-2


M-1M-2

WM-WI_1-_null_U01Umiejętność programowania dokumentów dynamicznych zwykorzystaniem wiodących technologii



C-1 S-1S-2


M-1M-2

WM-WI_1-_null_K01Kompenetecje z zakresie programowania dokumentówdynamicznych i pracy zespołowej



KnowledgeWM-WI_1-_null_W01 2,0 Nie zna metod i narzędzi programowania dokumentów dynamicznych

3,0 Zna podstawy modelu DOM i języka JavaScript

3,5 Zna podstawy modelu DOM, języka JavaScript, CSS

4,0 Zna szczegółowo model DOM, język JavaScript, CSS i XML

4,5 Zna szczegółowo model DOM, język JavaScript i XML i ma wiedzę na temat integracji tych narzędzi w ramach technologiiAJAX

5,0 Zna szczegółowo model DOM, język JavaScript i XML i ma szczegółową wiedzę na temat integracji tych narzędzi w ramachtechnologii AJAX. Ma wiedzę na temat specjalizowanych bibliotek programistycznych.

SkillsWM-WI_1-_null_U01 2,0 Nie potrafi programować dokumentów dynamicznych

3,0 Potrafi programować proste aplikacje w oparciu o DOM i język JavaScript

3,5 Potrafi programować proste aplikacje w oparciu o model DOM, język JavaScript i CSS

4,0 Potrafi programować aplikacje w oparciu o DOM, język JavaScript, CSS i XML

4,5 Umie programować aplikacje internetowe w oparciu o dokumenty dynamiczne i integrować technologie DOM, językJavaScript i XML w ramach AJAX. Umie dobrać technologie adekwatnie do problemu

5,0Umie programować aplikacje w oparciu o dokumenty dynamiczne i integrować technologie DOM, język JavaScript i XML wramach AJAX oraz umie korzystać z zaawansowanych bibliotek programistycznych. Umie dobrać technologie adekwatnie doproblemu.

Other social / personal competencesWM-WI_1-_null_K01 2,0 Nie spełnia kryteriów dla oceny 3

3,0 Ma świadomość istnienia wielu technologii stosowanych w programowaniu dokumentów dynamicznych

3,5 Potrafi wskazać kluczowe technologie zorientowane na dokumenty dynamiczne i z nich korzystać.

4,0 Potrafi wskazać kluczowe technologie zorientowane na dokumenty dynamiczne i z nich korzystać. Uzupełnia informacje wtym zakresie.

4,5 Potrafi wskazać kluczowe technologie zorientowane na dokumenty dynamiczne i z nich korzystać. Aktywnie uzupełniainformacje w tym zakresie.

5,0 5 Potrafi wskazać kluczowe technologie zorientowane na dokumenty dynamiczne i z nich korzystać. Aktywnie uzupełniainformacje w tym zakresie i poszukuje nowych rozwiązań.

Required reading1. Bogdan Brinzarea, AJAX and PHP: Building Modern Web Applications, PACKT, London, 2012

2. Anne Boehm, Zak Ruvalcaba, HTML5 and CSS3, Murach, NY, 2015

Supplementary reading1. Shawn M. Lauriat, Advanced Ajax: Architecture and Best Practices, Prentice Hall, NY, 2011

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Field of study

Mode of study


ECTS


Code

Electives

Language

Elective group


stationary

WI-1-ECO

3,0

credits english

ECTS (forms) 3,0

Level first cycle

Area(s) of study


Module

Course unit E-commerce and online marketing technologies






Jankowski Jarosław ([email protected])Leading teacher

Other teachers

PrerequisitesW-1 The basics of HTML language

Module/course unit objectivesC-1 Increasing the knowledge in the area of technologies used in electronic marketing

C-2 Increasing the knowledge in the area of strategies in electronic marketing

Course content divided into various forms of instruction Number of hoursT-L-1 Use of selected behavioral analysis systems for web site users 4

T-L-2 Configuration and planning of ad campaigns using ad servers 4

T-L-3 Use of contextual advertising and search engines 4

T-L-4 Search engine positioning 4

T-L-5 Use of selected social media platforms and social network analysis in marketing 4

T-L-6 Modeling diffusion of marketing messages in social networks 2

T-L-7 Use of e-commerce platforms and recommendation systems 4

T-L-8 Application of selected methods of extraction of knowledge in electronic marketing 4

T-W-1 Communication models in electronic marketing 2

T-W-2 Performance measurement and optimization of advertising campaigns 2

T-W-3 Marketing in social media 2

T-W-4 Search engine marketing 3

T-W-5 Marketing and email communication 2

T-W-6 Electronic commerce platforms and recommendation algorithms 2

T-W-7 Multivariate optimization and maximization of conversions 2

Student workload - forms of activity Number of hoursParticipation in classes 30A-L-1

Preparation of reports 10A-L-2

Consultation for laboratories 4A-L-3

Preparation for classes 15A-L-4



Consultation on the lecture 5A-W-3


E-commerce and online marketing technologies

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Teaching methods / toolsM-1 Lecture with presentations and examples.

M-2 Laboratory exercises and practical tasks.


S-1 Lecture: summary assessment. Written examination with practical questions, questions in the form of a choice anddescription.F

S-2 Laboratories: assessment based on reports and attendance.F



study





education

Teachingmethods


methods

Knowledge

C-1C-2

S-1S-2


M-1M-2

WM-WI_1-_null_W01Wiedza w zakresie wdrażania i eksploatacji systemówmarketingu elektronicznego.

T-W-1T-W-2T-W-3T-W-4T-W-5T-W-6

C-1C-2

S-1S-2

T-L-1T-L-6 M-1

M-2

WM-WI_1-_null_W02Wiedza w zakresie metod analitycznych, przetwarzania danych ialgorytmów wykorzystywanych w systemach marketinguelektronicznego.

T-W-7

Skills

C-1C-2

S-1S-2


M-1M-2

WM-WI_1-_null_U01Umiejętność wdrażania i eksploatacji systemów marketinguelektronicznego.

T-W-1T-W-2T-W-3T-W-4T-W-5T-W-6T-W-7

C-1C-2

S-1S-2

T-L-6M-1M-2

WM-WI_1-_null_U02Posiada umiejętność stosowania metod analitycznych ialgorytmów przetwarzania danych wykorzystywanych wsystemach marketingu elektronicznego.

T-W-7


C-1C-2

S-1S-2


M-1M-2

WM-WI_1-_null_K01Kompetencje w zakresie wdrażania i eksploatacji systemówmarketingu elektronicznego.

T-W-1T-W-2T-W-3T-W-4T-W-5T-W-6T-W-7

C-1C-2

S-1S-2

T-L-1T-L-3T-L-6

M-1M-2

WM-WI_1-_null_K02Komepetencje w zakresie zastosowań metod analitycznych wsystemach marketingu elektronicznego.

T-W-2T-W-7


KnowledgeWM-WI_1-_null_W01 2,0 Nie zna podstawowych pojęć związanych z technologiami marketingu elektronicznego.

3,0 Zna podstawowe pojęcia związane z technologiami marketingu elektronicznego.

3,5 Zna podstawowe pojęcia związane z technologiami marketingu elektronicznego. Zna podstawowe technologie stosowane wmarketingu elektronicznym.

4,0 Dobrze zna podstawowe pojęcia związane z technologiami marketingu elektronicznego. Dobrze zna technologie stosowane wmarketingu elektronicznym.

4,5Dobrze zna podstawowe pojęcia związane z marketingiem elektronicznym. Dobrze zna podstawowe technologie stosowanew marketingu elektronicznym. Zna metody i narzędzia stosowane w pomiarach efektywności systemów marketinguelektronicznego.

5,0Dobrze zna podstawowe pojęcia związane z technologiami marketingu elektronicznego. Dobrze zna podstawowe technologiestosowane w marketingu elektronicznym. Zna metody i narzędzia stosowane w pomiarach efektywności systemówmarketingu elektronicznego oraz metody optymalizacji powiązane z systemami internetowymi.

WM-WI_1-_null_W02 2,0 Nie zna podstawowych pojęć związanych z metodami analitycznymi stosowanymi w marketingu elektronicznym.

3,0 Zna podstawowe pojęcia związane z metodami analitycznymi stosowanymi w marketingu elektronicznym.

3,5 Zna podstawowe pojęcia i technologie powiązane z metodami analitycznymi stosowanymi w marketingu elektronicznym.

4,0 Dobrze zna podstawowe pojęcia i technologie powiązane z metodami analitycznymi stosowanymi w marketinguelektronicznym. Zna podstawowe algorymy i metody analityczne stosowane w tym obszarze.

4,5 Dobrze zna podstawowe pojęcia i technologie powiązane z metodami analitycznymi stosowanymi w marketinguelektronicznym. Zna algorytmy i narzędzia stosowane w pomiarach efektywności marketingu elektronicznego.

5,0 Bradzo dobrze zna pojęcia i technologie powiązane z metodami analitycznymi stosowanymi w marketingu elektronicznym.Dobrze zna algorytmy stosowane w pomiarach efektywności marketingu elektronicznego.

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SkillsWM-WI_1-_null_U01 2,0 Nie umie korzystać z systemów marketingu elektronicznego.

3,0 Umie korzystać z podstawowych funkcji systemów marketingu elektronicznego.

3,5 Umie korzystać z podstawowych funkcji marketingu elektronicznego. Umie konfigurować systemy marketinguelektronicznego.

4,0 Umie korzystać z podstawowych funkcji systemów marketingu elektronicznego. Umie konfigurować i wdrażaćsystemymarketingu elektronicznego. Umie dostosować ich funkcjonalność do potrzeb firmy.

4,5Umie korzystać z funkcji systemów marketingu elektronicznego. Umie konfigurować i wdrażać systemy marketinguelektronicznego. Umie dostosować ich funkcjonalność do potrzeb firmy. Umie zastosować narzędzia pomiarowe i systemyanalityczne.

5,0Umie korzystać z funkcji systemów marketingu elektronicznego. Umie konfigurować i wdrażać systemy marketinguelektronicznego. Umie dostosować ich funkcjonalność do potrzeb firmy. Umie zastosować narzędzia pomiarowe i systemyanalityczne. Umie zastosować metody optymalizacji systemów internetowych.

WM-WI_1-_null_U02 2,0 Nie umie nawet w podstawowym zakresie wykorzystać metod analityczne w marketingu elektronicznym.

3,0 Umie w podstawowym zakresie wykorzystać metody analityczne w marketingu elektronicznym.

3,5 Umie wykorzystać w marketingu elektronicznym podstawowe technologie powiązane z metodami analitycznymi.

4,0 Umie wykorzystać w marketingu elektronicznym podstawowe technologie powiązane z metodami analitycznymi. Umiewykorzystać podstawowe algorymy i metody analityczne stosowane w tym obszarze.

4,5 Umie wykorzystać w marketingu elektronicznym technologie powiązane z metodami analitycznymi. Umie wykorzystaćalgorymy i metody analityczne stosowane w tym obszarze.

5,0 Umie wykorzystać w marketingu elektronicznym zaawansowane technologie powiązane z metodami analitycznymi. Umiewykorzystać zaawansowane algorymy i metody analityczne stosowane w tym obszarze.

Other social / personal competencesWM-WI_1-_null_K01 2,0 Nie spełnia kryteriów dla oceny 3

3,0 Ma świadomość istnienia wielu technologii stosowanych w marketingu elektronicznym.

3,5 Ma świadomość istnienia wielu technologii stosowanych w marketingu elektronicznym i postępu technologicznego w tymobszarze. Potrafi wskazać kluczowe technologie.

4,0Ma świadomość istnienia wielu technologii stosowanych w marketingu elektronicznym i postępu technologicznego w tymobszarze. Potrafi wskazać kluczowe technologie. Uzupełnia informacje w tym zakresie. Ma świadomość istnienia ograniczeń iregulacji prawnych związanych z marketingiem elektronicznym.

4,5Ma świadomość istnienia wielu technologii stosowanych w marketingu elektronicznym. Potrafi wskazać kluczowetechnologie. Aktywnie uzupełnia informacje w tym zakresie na podstawie najnowszych źródeł krajowych i zagranicznych. Maświadomość istnienia ograniczeń i regulacji prawnych związanych z marketingiem elektronicznym.

5,0Ma świadomość istnienia wielu technologii stosowanych w e-biznesie. Potrafi wskazać kluczowe technologie. Aktywnieuzupełnia informacje w tym zakresie na podstawie najnowszych źródeł krajowych i zagranicznych i samodzielnie poszukujenowych rozwiązań. Ma świadomość istnienia ograniczeń i regulacji prawnych związanych z marketingiem elektronicznym.

WM-WI_1-_null_K02 2,0 Nie spełnia kryteriów dla oceny 3

3,0 Ma świadomość istnienia wielu algorytmów i metod analitycznych stosowanych w marketingu elektronicznym.

3,5 Ma świadomość istnienia wielu algorytmów i metod analitycznych stosowanych w marketingu elektronicznym. Potrafiwskazać kluczowe metody i technologie.

4,0 Ma świadomość istnienia wielu algorytmów i metod analitycznych stosowanych w marketingu elektronicznym. Potrafiwskazać kluczowe metody i technologie. Uzupełnia informacje w tym zakresie.

4,5Ma świadomość istnienia wielu algorytmów i metod analitycznych stosowanych w marketingu elektronicznym. Potrafiwskazać kluczowe metody i technologie. Uzupełnia informacje w tym zakresie. Ma świadomość istnienia ograniczeń iregulacji prawnych związanych z marketingiem elektronicznym.

5,0Ma świadomość istnienia wielu algorytmów i metod analitycznych stosowanych w marketingu elektronicznym. Potrafiwskazać kluczowe metody i technologie. Uzupełnia informacje w tym zakresie. Ma świadomość istnienia ograniczeń iregulacji prawnych związanych z marketingiem elektronicznym. Aktywnie uzupełnia informacje w tym zakresie na podstawienajnowszych źródeł krajowych i zagranicznych i samodzielnie poszukuje nowych rozwiązań.

Required reading1. Kenneth C. Laudon, Carol Guercio Traver, E-Commerce, Pearson, NY, 2017

2. Rob Stokes, eMarketing: The essential guide to marketing in a digital world, QUIRK, London, 2014

Supplementary reading1. Dave Chaffey, PR Smith, Emarketing Excellence: Planning and Optimizing your Digital Marketing, Routledge, London, 2013

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Field of study

Mode of study


ECTS


Code

Electives

Language

Elective group


stationary

WI-1-EEG

4,0

credits english

ECTS (forms) 4,0

Level first cycle

Area(s) of study


Module

Course unit EEG signal analysis in Matlab









Module/course unit objectivesC-1 To teach students how to record, process and analyze EEG signals in Matlab environments.

Course content divided into various forms of instruction Number of hoursT-L-1 Introduction to Matlab programming 10

T-L-2 OpenVibe platform 6

T-L-3 Sending data from OpenVibe to Matlab 8

T-L-4 Recording EEG signals with 19-channel Discovery 20 device 4

T-L-5 Removing artifacts from EEG signal 4

T-L-6 Spatial and temporal filtering 5

T-L-7 Extracting different brain activity patterns from EEG recording 6

T-L-8 Exam. 2

T-W-1 EEG signals - main characteristics 3

T-W-2 Main types of artifacts and methods for removing them 4

T-W-3 Spectral analysis of EEG signal (Fourier transform) 2

T-W-4 Extracting different brain activity patterns from EEG recording 4

T-W-5 Exam. 2






M-2 Discussion.



S-1 The final report describing the detailed results of the analysis of the EEG signal acquired durings laboratories andprocessed in Matlab environment.P


EEG signal analysis in Matlab

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study





education

Teachingmethods


methods

Knowledge

C-1 S-2

T-W-1T-W-2

M-1M-2


T-W-3T-W-4

Skills

C-1 S-1T-L-1T-L-2T-L-3T-L-4


T-L-5T-L-6T-L-7





3,54,04,55,0


3,0 The student is able to acquire EEG signal and perform its spectral anaysis in Matlab environment.

3,54,04,55,0


Required reading1. Lotte F., Study of Electroencephalographic Signal Processing and Classification Techniques towards the use of Brain-ComputerInterfaces in Virtual Reality Applications, 2008, PhD Thesis, https://sites.google.com/site/fabienlotte/phdthesis2. S. W. Smith, Digital Signal Processing: A practical Guide for Engineers and Scientists, 2003

3. Official Matlab site: http://www.mathworks.com/help/matlab/

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Field of study

Mode of study


ECTS


Code

Electives

Language

Elective group


stationary

WI-1-EMS

4,0

credits polish

ECTS (forms) 4,0

Level first cycle

Area(s) of study


Module

Course unit Embedded systems







Other teachers

PrerequisitesW-1 Computer systems architecture

W-2 Programming basics

Module/course unit objectivesC-1 The ability to classify, describe and build microcontroller based embedded systems

Course content divided into various forms of instruction Number of hoursT-W-1 Introduction to embedded systems: real time issues, power consumptions, software architectures. 2

T-W-2 Popular microcontroler architectures (e.g. AVR, ARM) 6

T-W-3 Main peripheral modules used in microcontroller 6

T-W-4 Selected input/output devices (displays, keyboards, a/c and c/a converters, motors, sensors) andcommunication interfaces. 6

T-W-5 Single board computers and embedded operating systems 6

T-W-6 Reconfigurable devices in embedded control and compputing. 2

T-W-7 Exam 2

T-L-1 Lab work organization, tools and equipment presentation. 2

T-L-2 Arduino as a popular embedded system. 2

T-L-3 Selected application for Arduino board. 4

T-L-4 Develpment environment and assembler in embedded systems 2

T-L-5 Introduction to C programming using selected microcontroller platform. 2

T-L-6 LEDs and LED display handling 2

T-L-7 Switches, keyboard and debouncing. 2

T-L-8 Building own system using peripheral modules like UART, LCD display, a/c and c/a converters, audioinput/output etc. 6

T-L-9 Single board computers and embedded operating systems 4

T-L-10 Reconfigurable embedded systems and soft processors. 4

Student workload - forms of activity Number of hoursLecture attendance 30A-W-1

Self-study 30A-W-2

Labs attendance 30A-L-1

Reports and projects preparation 30A-L-2


Embedded systems

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Teaching methods / toolsM-2 Laboratory


S-2 Lab reportsF



study





education

Teachingmethods


methods

Knowledge

C-1 S-1T-W-1T-W-2T-W-3T-W-4

M-1WM-WI_1-_??_W01The students is able to describe, classify and analyze embeddedsystems based on selected microcontrollers with or withoutoperating systems.

T-W-5T-W-6T-W-7

Skills

C-1 S-2


M-2WM-WI_1-_??_U01The student can implement and build simple embedded systemsdue to the functional requirements.

T-L-6T-L-7T-L-8T-L-9T-L-10




3,0 The student has basic knowledge about microcontrollers, embedded systems, desingining methods and tools.

3,54,04,55,0


3,0 The student is able to implement simple embedded systems based on microcontroller with or without operating systemusing required peripherals.

3,54,04,55,0


Required reading1. Microcontroller vendors, Selected microcontroler documentetion, 2011

2. Joseph Yiu, The Definitive Guide to ARM Cortex-M0 and Cortex-M0+ Processors, Elsevier, 2015

3. Edward A. Lee, Sanjit A. Seshia, Introduction to embedded systems. A cyber-physical systems approach., MIT Press, 2017

[ logo uczelni ]

Field of study

Mode of study


ECTS


Code

Electives

Language

Elective group


stationary

WI-1-EFL

6,0

credits english

ECTS (forms) 6,0

Level first cycle

Area(s) of study


Module

Course unit Essentials of fuzzy logic and its application to systemmodeling and control






Pluciński Marcin ([email protected])Leading teacherKołodziejczyk Joanna ([email protected]), Korzeń Marcin([email protected]), Sałabun Wojciech ([email protected])Other teachers

PrerequisitesW-1 Basic knowledge of high mathematics


C-1 Acquirement of competence and practice in construction of fuzzy models of systems, fuzzy calculations and fuzzy control ofplants

Course content divided into various forms of instruction Number of hoursT-L-1 Discovering by student fuzzy phenomena, fuzzy variables, fuzzy notions in the world 2

T-L-2Constructing membership functions for own detected uncertain values from science, technique,medicine, economics, biology etc. Describing membership fumctions by mathematical formulas.Trasformation of vertical membership functions into horizontal functions.

3

T-L-3 Constructing rule bases for real systems and checking their logical consistence 2

T-L-4Training in fuzzyfication, rule premises evaluation, conclusion activation of individual rules,aggregation of individual rule conclusions in one resulting conclusion of the rule base and itsdefuzzification

4

T-L-5 Constructing fuzzy models for real systems. 4

T-L-6 Calculation of the fuzzy model output for given values of its inputs for models of various dimensionality 4

T-L-7 Constructing neuro-fuzzy networks for a given fuzzy model 2

T-L-8 Constructing expert fuzzy controllers for a given real plant 6

T-L-9 Constructing fuzzy controllers on the basis of a plant-model 3

T-W-1 Diffrence between classical and fuzzy logic. Examples of fuzziness in the real world. Necessity offuzziness use. Short history of fuzzy logic development. 4

T-W-2Mathematical models of fuzzy linguistic and numerical evaluations : membership functions. Examplesof membership functions. Vertical and horizontal models of membership functions. Identification ofmembership functions by experts. Typical errors made during the identification.

4

T-W-3 Classical (vertical) and new, horizontal fuzzy arithmetic. Transformation of vertical in horizontalmembership functions. Examples of calculations. Granular arithmetic and mathematics. 4

T-W-4Fuzzy models of systems. Components of fuzzy models: fuzzification, premise evaluation,determination of activated membership functions of paricular rules, determining of the resultingmembership function of the rule base and its defuzzification.

6

T-W-5 Constructing fuzzy models for chosen real problems and calculating model ouputs for give modelinputs. 4

T-W-6 Neuro-fuzzy networks as self-learning fuzzy models. 2

T-W-7 Fuzzy control and its structure. Classic, expert fuzzy control and control based on the model of thecontrolled plant. 4

T-W-8 Examples of real applications of fuzzy logic. 2


Essentials of fuzzy logic and its application to system modeling and control

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Student workload - forms of activity Number of hoursParticipating in laboratory training 30A-L-1

Solving home tasks given by the academician 40A-L-2

Studying literature referring to the laboratory problems 25A-L-3

Consultations connected with laboratory problems solved by the student 25A-L-4

Participating in the lectures 30A-W-1

Home studying of the lecture text and of reccomended literature 26A-W-2

Personal consultations devoted to explanation of difficult parts of lectures 4A-W-3

Teaching methods / toolsM-1 Informational lecture with presentations

M-2 Laboratory training in individual solving of problems delivered by an academition


S-1 Lectures: summarizing evaluation of knowledge assimilated by student in form of an exam and of evaluation thestudent activity shown during lecturesP

S-2 Laboratory: forming evaluation of the student based on the student activity and ability shown at solving problemsgiven by an academicianF



study





education

Teachingmethods


methods

Knowledge

C-1 S-1T-W-1T-W-2T-W-3T-W-4

M-1WM-WI_1-_??_W01The student has knowledge about fuzzy sets, fuzzy modellingand their practical applications.


Skills

C-1 S-2


M-2WM-WI_1-_??_U01The student has the ability to analyse fuzzy models work, tocreate them for chosen real problems, and to use them incontrol systems.





3,0 The student has the basic knowledge about fuzzy systems and fuzzy modelling.

3,54,04,55,0


3,0 The student has the basic ability to create fuzzy models and to analyse their work.

3,54,04,55,0


Required reading1. Andrzej Piegat, Fuzzy modeling and control, Physica-Verlag, A Springer-Verlag Company, 2001, 1

2. Witold Pedrycz, Fernando Gomide, Fuzzy systems engineering, Wiley-Interscience, Hoboken, New Jersey, USA, 2007, 1

Supplementary reading1. Y. Bai, H. Zhuang, D. Wang (editors), Advanced fuzzy logic technologies in industriel applications, Springer, Berlin, Heidelberg, NewYork, 2006, 1

[ logo uczelni ]

Field of study

Mode of study


ECTS


Code

Electives

Language

Elective group


stationary

WI-1-ESY

4,0

credits english

ECTS (forms) 4,0

Level first cycle

Area(s) of study


Module

Course unit Expert systems







Other teachers

PrerequisitesW-1 Algorithms and data structures

Module/course unit objectivesC-1 To learn the basic knowledge in expert systems. Student will have the ability to recognize areas of implementation.

C-2 Students will be able to design, build and implement rule-based expert systems.

Course content divided into various forms of instruction Number of hoursT-L-1 CLIPS - installing and dealing with facts 2

T-L-2 Rules constract in CLIPS 4

T-L-3 Excerises with simple user interface communication in CLIPS 6

T-L-4 Functions and advanced CLIPS programming 6

T-L-5 Project in CLIPS 5

T-L-6 From CLIPS to JESS 7

T-W-1 History of Expert Systems. The begining, early solutions. 2

T-W-2 Fomal representation of knowladge in expert systems. Dealing with uncertainty. 2

T-W-3 Propositional logic as a method of knowladge representation. 2

T-W-4 First predicate logic. Prolog programming language. 3

T-W-5 Uncetrainty - probablistic view. Bayes theorem and bayesian networks. 2

T-W-6 Fuzzy expert systems. 2

T-W-7 Expert systems based on certainty factor. 2

Student workload - forms of activity Number of hoursLab participation 30A-L-1

Study the literature 20A-L-2

Working on homeworks 34A-L-3

Lecture participation 15A-W-1

Study the literature 15A-W-2

Preparing for test 6A-W-3

Teaching methods / toolsM-1 Presentation, lecture

M-2 Discussion durig lecture.

M-3 Developing software in CLIPS

Expert systems

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Evaluation methods (F - progressive, P - final)S-1 Test checking the knowledge on expert systemsP

S-2 Short programming tasks in CLIPSF

S-3 Programming project - make your own expert systemP



study





education

Teachingmethods


methods

Knowledge

C-1 S-1


M-1M-2

WM-WI_1-_??_W01Student understand a structure of the expert system. Has aknowladge on representation forms and how the uncertatintycould be represented. Can name and explain how well-knownexpert systems work.

T-W-5T-W-6T-W-7

Skills

C-2 S-2S-3

T-L-1T-L-2T-L-3

M-3WM-WI_1-_??_U01Students has the ability to develop expert systems in CLIPS andJESS.

T-L-4T-L-5T-L-6




3,0 Basic knowledge on expert systems.

3,54,04,55,0


3,03,54,04,55,0


Required reading1. Russel S., Norvig P, Artificial Intelligence A modern approach, Prentice Hall, 2003

2. Clips online documentation, 2016

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Field of study

Mode of study


ECTS


Code

Electives

Language

Elective group


stationary

WI-1-FPL

4,0

credits english

ECTS (forms) 4,0

Level first cycle

Area(s) of study


Module

Course unit F# Programming Language







Other teachers


Module/course unit objectivesC-1 Familiar with the sytnax, structures and principles used in the f# language

C-2 The ability to develop a program in f# language.

Course content divided into various forms of instruction Number of hoursT-L-1 Introduction to visual Studio IDE and F# 2

T-L-2 Declaring values and functions, pattern matching basics 2

T-L-3 Recursive and higher order functions 2

T-L-4 Option types, tuples and records 2

T-L-5 Lists and sequences 4

T-L-6 Sets, maps and discriminated unions 2

T-L-7 Control flows 2

T-L-8 Arrays 2

T-L-9 Mutable data and mutable collections 2

T-L-10 I/O operations 2

T-L-11 Classes and operator overloding 2

T-L-12 Inheritance and interfaces 2

T-L-13 F# advanced 4

T-W-1 Introduction to: Functional Programming and F# programming language 2

T-W-2 Working With Functions 2

T-W-3 Immutable Data Structures 4

T-W-4 Imperative Programming 2

T-W-5 Object Oriented Programming 2

T-W-6 F# Advanced 2

T-W-7 Exam 1




F# Programming Language

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Student workload - forms of activity Number of hoursStudent individual work 15A-W-2


M-2 Discussion



S-2 written examP



study





education

Teachingmethods


methods

Knowledge

C-1 S-2T-W-1T-W-2T-W-3

M-1M-2

WM-WI_1-_null_W01After the lecture the student will know the f# syntax and will beable to define programming concepts used in the f# language.

T-W-4T-W-5T-W-6

C-2 S-2T-W-1T-W-2T-W-3

M-1M-2

WM-WI_1-_null_W02After the lecture the student will be able to explain what ishappening in a f# code.

T-W-4T-W-5T-W-6

Skills

C-1C-2 S-1


M-2M-3

WM-WI_1-_null_U02The student will be able to write program in a f# language.

T-L-8T-L-9T-L-10T-L-11T-L-12T-L-13




3,0 The student knows f# syntax.

3,54,04,55,0

WM-WI_1-_null_W02 2,03,0 The student is able to explain code of a simple program written in f#.

3,54,04,55,0


3,0 The student is able to write a simple program in a f# language.

3,54,04,55,0


Required reading1. Robert Pickering, Beginning F#, 2009

2. Don Syme, Adam Granicz, Antonio Cisternino, Expert F#, 2007

Supplementary reading1. Jon Harrop, F# for Scientists, 2008

2. https://en.wikibooks.org/wiki/F_Sharp_Programming

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Field of study

Mode of study


ECTS


Code

Electives

Language

Elective group


stationary

WI-1-FDC

4,0

credits english

ECTS (forms) 4,0

Level first cycle

Area(s) of study


Module

Course unit FPGA Design and reconfigurable computing







Other teachers

PrerequisitesW-1 Digital design.

W-2 Basics of electronics.

Module/course unit objectivesC-1 FPGA programming in Verilog.

C-2 Basics of VHDL.

C-3 General knowledge of FPGA technology.

Course content divided into various forms of instruction Number of hoursT-L-1 FPGA - basics of Verilog. 2

T-L-2 FPGA - VGA display. 6

T-L-3 FPGA - motor control + encoder. 6

T-L-4 CPLD - low power programming. 2

T-L-5 FPGA editor. 2

T-L-6 FPGA - audio processing + DSP resources. 4

T-L-7 Project. 6

T-L-8 Picoblaze - soft processor. 2

T-W-1 Basics of FPGA/CPLD devices architectures. 2

T-W-2 Verilog language. 4

T-W-3 VHDL language. 2

T-W-4 SystemVerilog and TLM (Transaction Level Modeling) 2

T-W-5 Synthesis methodology. 2

T-W-6 Detailed FPGA structure of modern devices. 3

Student workload - forms of activity Number of hoursLaboratories. 24A-L-1

Project 36A-L-2

Individual work 30A-L-3

Lectures 15A-W-1



FPGA Design and reconfigurable computing

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Teaching methods / toolsM-2 Laboratories.

M-3 Project

Evaluation methods (F - progressive, P - final)S-1 Final ExamP

S-2 Laboratory reports.F

S-3 Project.F



study





education

Teachingmethods


methods

KnowledgeWM-WI_1-_??_W01Student knows basics of HDL and RTL synthesis.WM-WI_1-_??_W02Student knows structures of FPGA devices.

SkillsWM-WI_1-_??_U01Student is able to program in Verilog/VHDL.




3,03,54,04,55,0

WM-WI_1-_??_W02 2,03,03,54,04,55,0


3,03,54,04,55,0


Required reading1. C.M. Maxfield, The Design Warrior’s Guide to FPGAs, Linacre House2. S. Sutherland, S. Davidmann, P. Flake, SystemVerilog for Design, A Guide to Using SystemVerilog for Hardware Design and Modeling,Springer, 2011

Supplementary reading1. K.K. Parhi, VLSI Digital Signal Processing Systems, John Wiley & Sons, 2011

2. S. S. Bhattacharyya, Hardware/Software Co-synthesis of DSP Systems, Programmable Digital Signal Processors, 2001

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Field of study

Mode of study


ECTS


Code

Electives

Language

Elective group


stationary

WI-1-FEC

3,0

credits english

ECTS (forms) 3,0

Level first cycle

Area(s) of study


Module

Course unit Fundamentals of Error-Correcting Block Codes




lecturing course 1W, 2S 15 1,5 0,50 creditsA


Majorkowska-Mech Dorota ([email protected])Leading teacher

Other teachers

PrerequisitesW-1 Basics of linear and abstract algebra.

Module/course unit objectivesC-1 Knowledge of error-correcting codes

C-2 Skills in error-correcting codes construction

Course content divided into various forms of instruction Number of hoursT-A-1 Calculation of the minimum distance, detection and correction capability of line code codes. 2

T-A-2 Examination of the properties of algebraic structures. 2

T-A-3 Construction of extended Galois fields. 2

T-A-4 Finding primitive elements of extended Galois fild, minimal polynomials and conjugates of elements. 2

T-A-5 Linear block codes: matrix description, standard array, syndrome. Constructing of Hamming codes. 2

T-A-6 Cyclic codes: polynomial and matrix description of cyclic codes, encoding, syndrome computation,error detection and decoding. Constructing some examples of cyclic codes. 3

T-A-7 Written test. 2

T-W-1 The discrete communication channel. Types of errors and types of error-correcting codes.Block codes, minimum distance, error-detecting and error-correcting capabilities of a block code. 2

T-W-2 Algebraic structures: groups, rings, fields, vector spaces. 2

T-W-3 Construction of extended Galois fields. 2

T-W-4 Structure of extended Galois fields, primitive elements, minimal polynomials and conjugates. 2

T-W-5 Linear block codes: matrix description, standard array, syndrome, Hamming codes, Hamming spheresand perfect codes. 2

T-W-6 Cyclic codes: polynomial and matrix description of cyclic codes, encoding, syndrome computation,error detection and decoding. Important classes of cyclic codes. 4

T-W-7 Written exam. 1

Student workload - forms of activity Number of hoursparticipations in classes 15A-A-1

homeworks 15A-A-2

self study 15A-A-3

participations in lectures 15A-W-1

self study 30A-W-2


M-2 Solving problems on board (workshop)

Fundamentals of Error-Correcting Block Codes

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S-2 Written testP



study





education

Teachingmethods


methods

Knowledge

C-1 S-1M-1WM-WI_1-_??_W01Students has knowledge in error-correcting codes construction

Skills

C-2 S-2M-2WM-WI_1-_??_U01Students has skills in error-correcting codes construction




3,03,54,04,55,0


3,03,54,04,55,0


Required reading1. Richard E. Blahut, Algebraic Codes for Data Transmission, Cambridge University Press, New York, 2003

Supplementary reading1. Shu Lin, Daniel J. Costello, Error Control Coding: Fundamentals and Applications, Pearson-Prentice Hall, 2004

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Field of study

Mode of study


ECTS


Code

Electives

Language

Elective group


stationary

WI-1-GUI

3,0

credits english

ECTS (forms) 3,0

Level first cycle

Area(s) of study


Module

Course unit Graphical User Interface in .NET







Other teachers


Module/course unit objectivesC-1 Familiar with Windows Forms and Windows Presentation Foundation

C-2 The ability to develop Windows Form Application and Windows Presentation Foundation Application.

Course content divided into various forms of instruction Number of hoursT-L-1 Introduction to Windows Forms 1

T-L-2 Controls, Forms, Containers and Applications, Menus, Toolbars, Dialogs 2

T-L-3 Settings, Resources 2

T-L-4 Building Controls, Inheritance and Reuse, Property Grids, Data binding 2

T-L-5 Introduction to Windows Presentation Foundation 1

T-L-6 XAML 1

T-L-7 Sizing, Positioning and Transforming Elements, Layout with Panels 2

T-L-8 Input Events, Content Controls, Item Controls 2

T-L-9 Image, Text, Other Controls, Resources, Data Binding 2

T-W-1 Windows Forms Fundamentals 2

T-W-2 Custom Controls 2

T-W-3 Modern Controls 2

T-W-4 Data Binding and Windows Forms Techniques 2

T-W-5 Building a WPF Application 2

T-W-6 WPF Controls 2

T-W-7 Data Binding and Rich Media 2

T-W-8 Exam 1






Graphical User Interface in .NET

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Teaching methods / toolsM-2 Discussion



S-2 written examP



study





education

Teachingmethods


methods

Knowledge

C-1 S-2T-W-1T-W-2 M-1

M-2WM-WI_1-_null_W01After the course the student will possess knowledge aboutWindows Forms

T-W-3T-W-4

C-1 S-2T-W-5T-W-6 M-1

M-2WM-WI_1-_null_W02After the course the student will possess knowledge aboutWindows Presentation Foundation

T-W-7

Skills

C-2 S-1T-L-1T-L-2 M-2

M-3WM-WI_1-_null_U01After the course students will be able to design and createWindows Form Application

T-L-3T-L-4

C-2 S-1T-L-5T-L-6T-L-7

M-2M-3

WM-WI_1-_null_U02After the course students will be able to design and createWindows Presentation Foundation Application.

T-L-8T-L-9




3,0 The student possesses basic knowledge about Windows Forms and common controls

3,54,04,55,0

WM-WI_1-_null_W02 2,03,0 The student possesses basic knowledge about Windows Presentation Foundation

3,54,04,55,0


3,0 The student will be able to write a simple Windows Form Application

3,54,04,55,0

WM-WI_1-_null_U02 2,03,0 The student will be able to write a simple Windows Presentation Foundation Application

3,54,04,55,0


Required reading1. Chris Sells, Windows Forms Programming in C#, 2003

2. Matthew MacDonald, Pro .NET 2.0 Windows Forms and Custom Controls in C#, 2005

3. Adam Nathan, WPF 4.5 Unleashed, 2013

Supplementary reading1. Andrew Troelsen, Philip Japikse, C# 6.0 and the .NET 4.6 Framework, 2015

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Field of study

Mode of study


ECTS


Code

Electives

Language

Elective group


stationary

WI-1-GUA

4,0

credits english

ECTS (forms) 4,0

Level first cycle

Area(s) of study


Module

Course unit Graphics user interfaces in Android








PrerequisitesW-1 Programming skills in C/C++ languages.

Module/course unit objectivesC-1 Gaining knowledge, skills, and competences on designing and implementation of GUI in Androis OS.


T-P-1 Implementation of a project involving the development of the example GUI program based on the Qttoolkit. 30

T-W-1 Introduction to graphics user interfaces (GUI). 1

T-W-2 GUI programming paradigms. 2

T-W-3 Introduction to Qt toolkit. 1

T-W-4 Compilation and deployment for Android. 1

T-W-5 Qt IDE (integrated development environment). 2

T-W-6 Qt class hierarchy, QWindow and QWidget. 2

T-W-7 Widgets. 4

T-W-8 Signals and slots. 2

Student workload - forms of activity Number of hoursParticipation in workshops. 30A-P-1

Implementation of GUI program as a part of homework. 60A-P-2




M-1 LectureWorkshops

Evaluation methods (F - progressive, P - final)S-1 Evaluation of prepared program with GUIP



study





education

Teachingmethods


methods

Knowledge

C-1 S-1T-W-1T-W-2T-W-3T-W-4

M-1WM-WI_1-_??_W01Gaining knowledge on designing and implementaion of GUI inAndroid


Graphics user interfaces in Android

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Skills

C-1 S-1T-P-1 M-1WM-WI_1-_??_U01Gaining skills on designing and implementaion of GUI in Android


C-1 S-1T-P-1

M-1WM-WI_1-_??_K01Gaining competences on designing and implementaion of GUI inAndroid




3,54,04,55,0



3,54,04,55,0



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Required reading1. Jasmin Blanchette and Mark Summerfield, C++ GUI Programming with Qt 4, Prentice Hall, 2008, 2

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Field of study

Mode of study


ECTS


Code

Electives

Language

Elective group


stationary

WI-1-HMM

3,0

credits english

ECTS (forms) 3,0

Level first cycle

Area(s) of study


Module

Course unit Hidden Markov Models and its Applications







Other teachers

PrerequisitesW-1 Basics of statistics and higher mathematics.

W-2 Basic knowledge about artificial intelligence.

Module/course unit objectivesC-1 Theoretical knowledge about Markov Models.

C-2 The ability to apply Markov Models in patter recognition tasks.

Course content divided into various forms of instruction Number of hoursT-L-1 Solving pattern recognition problems with Markov Chain 2

T-L-2 Developing own implementation of Markov Chain 2

T-L-3 Solving pattern recognition problems with Hidden Markov Model 5

T-L-4 Developing own implementation of Hidden Markov Model 4

T-L-5 Solving pattern recognition problem with Continuous Observation Densities HMM 2

T-W-1 Intorduction to Markov Models, "Observable" Markov Model 2

T-W-2 Fundamentals of Hidden Markov Models (HMM) 2

T-W-3 Forward-backward algorithm, Viterbi Algorithm 2

T-W-4 Baum-Welch Reestimation method 2

T-W-5 Implementation issues for HMM: variables scaling, multiple observations sequences 2

T-W-6 Continuous Observation Densities in HMM 2

T-W-7 Mixture HMM 2

T-W-8 Exam 1






M-2 Discussion



Hidden Markov models and its applications

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S-2 written examP



study





education

Teachingmethods


methods

Knowledge

C-1 S-2T-W-1T-W-2T-W-3T-W-4

M-1M-2

WM_1-_??_W01After the course the student will possess knowledge about theconstruction, internal algorithms and applications of MarkovModels, Hidden Markov Models and its modifications.

T-W-5T-W-6T-W-7

Skills

C-2 S-1T-L-1T-L-2T-L-3

M-2M-3

WM_1-_??_U01After the course students will be able to make use of HMM inpatter recognition tasks.

T-L-4T-L-5



KnowledgeWM_1-_??_W01 2,0

3,0 The student possesses basic knowledge about Markov Models, internal algorithms and its applications.

3,54,04,55,0

SkillsWM_1-_??_U01 2,0

3,0 The student is able to use Markov Models in a simple pattern recognition task.

3,54,04,55,0


Required reading1. Ming Liao, Applied Stochastic Processes, 2013

2. Andrew M. Fraser, Hidden Markov Models and Dynamical Systems, 2008

3. Gernot A. Fink, Markov Models for Pattern Recognition: From Theory to Applications, 2008

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Field of study

Mode of study


ECTS


Code

Electives

Language

Elective group


stationary

WI-1-HCI

3,0

credits english

ECTS (forms) 3,0

Level first cycle

Area(s) of study


Module

Course unit Human-Computer Interaction







Other teachers

PrerequisitesW-1 Elementary programming skills


C-1 The main objective of the course is to familiarize students with the current trends in human-computer interaction. Newapproaches like touchless interaction as well as classical methods are discussed and analyzed during the course.

C-2 Students are familiarized with the wide range of modern equipment, software and algorithms of human-computerinteraction.


T-L-1

Introduction to human-computer interaction.Improving everyday computing: mouse gestures, virtual assistants, etc.Detection and recognition of the user.Who is the user? – assessment of sex, age and emotional state.Touchless interaction: gestures recognition, hand operated interfaces, head operated interfaces,touchless text entry.Eyetracking - determining the areas of interest on the screen.Assistive technologies for user with disabilities.

15

T-W-1


15





Design and implementation of own human-computer interaction system. 19A-L-5







Human-Computer Interaction

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Evaluation methods (F - progressive, P - final)S-1 Final grade based on continuous assessment of tasks carried out during the classes.P




study





education

Teachingmethods


methods

KnowledgeWM-WI_1-_??_W01Students are familarized with the current trends in human-computer interaction. They gain knowledge about newapproaches like touchless interaction as well as classicalmethods.

SkillsWM-WI_1-_??_U01Students are familiarized with the wide range of modernequipment, software and algorithms of human-computerinteraction.

Other social / personal competencesWM-WI_1-_??_K01Student has the consciousness of building communicationsystems in the strict connection with a social group that is theaddressee of the given solutions (culture, norms, status).Student is aware of the responsibility for the wronginterpretation of the communication message.



3,03,54,04,55,0


3,03,54,04,55,0


3,03,54,04,55,0

Required reading1. A. Dix, J. Finlay, G. D. Abowd, R. Beale, Human-Computer Interaction, Pearson, 2004, 3rd Edition2. B. Shneiderman, C. Plaisant, Designing the User Interface: Strategies for Effective Human-Computer Interaction, Pearson Addison-Wesley, 2009, 5th Edition3. D. K. Kumar, S. P. Arjunan, Human-Computer Interface Technologies for the Motor Impaired, CRC Press, 2015

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Field of study

Mode of study


ECTS


Code

Electives

Language

Elective group


stationary

WI-1-IDS

6,0

credits english

ECTS (forms) 6,0

Level first cycle

Area(s) of study


Module

Course unit Intelligent Decision Systems







Piegat Andrzej ([email protected])Other teachers


Module/course unit objectivesC-1 To provide the knowledge about multi-criteria decision-making methods which are used to solving decision problems

C-2 To equip the students with the ability of solving decision problems by using MCDM methods

Course content divided into various forms of instruction Number of hoursT-L-1 Intro to solving decision problems by using WSM and WPM methods 2

T-L-2 Intro to solving decision problems by using TOPSIS methods 4

T-L-3 Intro to solving decision problems by using AHP methods 5

T-L-4 Intro to solving decision problems by using ELECTRE methods 4

T-L-5 Intro to solving decision problems by using ANP methods 4

T-L-6 Intro to solving decision problems by using Fuzzy Logic 10

T-L-7 Exam 1

T-W-1 Description of decision making problems (structure, elements etc.) 3

T-W-2 Review of the MCDM methods (achievements and main directions of researches) 3

T-W-3 The WSM and WPM methods (examples, application, benefits, defects, etc.) 2

T-W-4 The AHP and ANP methods (examples, application, benefits, defects, etc.) 6

T-W-5 The ELECTRE methods (examples, application, benefits, defects, etc.) 4

T-W-6 The TOPSIS methods (examples, application, benefits, defects, etc.) 4

T-W-7 The Fuzzy methods in decision-making (examples, application, benefits, defects, etc.) 7

T-W-8 Exam 1






M-2 Discussion


Intelligent Decision Systems

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S-2 Written examP



study





education

Teachingmethods


methods

Knowledge

C-1C-2 S-2


M-1M-2

WM-WI_1-_null_W01After the lectures the student will be able to define a MCDMproblem, describe main MCDM methods, and choose the methodsuitable for a decision problem

T-W-5T-W-6T-W-7

Skills

C-2 S-1T-L-1T-L-2T-L-3

M-2M-3

WM-WI_1-_null_U01The student will be able to choose MCDM method for a problem.

T-L-4T-L-5T-L-6

C-2 S-1T-L-1T-L-2T-L-3

M-2M-3

WM-WI_1-_null_U02The student will be able to solve a multi-criteria problem.

T-L-4T-L-5T-L-6




3,0 The student is able to define the MCDM methods and problems concept

3,54,04,55,0


3,0 The student is able to choose an appropriate MCDM method to a specific decision problem

3,54,04,55,0

WM-WI_1-_null_U02 2,03,0 The student is able to solve a specific decision problem

3,54,04,55,0



[ logo uczelni ]

Field of study

Mode of study


ECTS


Code

Electives

Language

Elective group


stationary

WI-1-IAI

3,0

credits english

ECTS (forms) 3,0

Level first cycle

Area(s) of study


Module

Course unit Introduction to Artificial Intelligence







Kołodziejczyk Joanna ([email protected])Other teachers



W-3 programming

W-4 object oriented programming

Module/course unit objectivesC-1 Familiarization with various search techniques for practical problems.

C-2 Introducing elements of two-person games of perfect information and algorithms for that purpose.

C-3 Building up the understing of such notions as: heuristics, pay-off, strategy, search horizon.

C-4 Familiarization with classification and approximation as exemplary tasks within machine learning. Introducing simpleartificial neural networks for that purpose.

C-5 Teaching a possibility of solving optimization problems by means of randomized methods (genetic algorithms).

C-6 Giving a historical background on AI and problems within it.


T-L-1 Gatting familiar with Java, Eclipse IDE, and a set of classes prepared for implementations of searchalgorithms. Initial implementation of the sudoku solver. 2

T-L-2Implementation of sudoku solver. Testing - varations on the initial state (making the sudoku harder).Observing the number of visited states and the number of solution.Posing the homework task - programming the solver for the sliding puzzle.

2

T-L-3Testing homework programs - sliding puzzle solvers. Getting familiar with Java classes prepared forgame tree searches (alpha-beta pruning engine). Posing the homework task - programming an AIplaying the connect4 game.

3

T-L-4 Testing homework programs - connect4 program: experimentations with different search depths,program vs program games, comments on introduced heuristics (position evaluation). 2

T-L-5Programming the simple perceptron (in MATLAB). Two-class separation of points on a plane. Observingthe number of update steps in learning algorithm influenced by: learning rate coefficient, number ofdata points (sample size), changes in separation margin. Posing the homework task - implementationof non-linear separation using the simple perceptron together with the kernel trick.

2

T-L-6Implementation of MLP neural network (in MATLAB) for approximation of a function of two variables.Testing accuracy with respect to: number of neurons, learning coefficient, number of update steps.Posing the homework task: complexity selection for MLP via cross-validation.

2

T-L-7Genetic algorithm implementation for the knapsack problem, including: at least two selection methods,and two crossing-over methods. Posing the homework task: comparison of GA solutions with exactsolutions based on dynamic programming (computation times).

2

T-W-1Definitions of AI and problems posed within it, e.g.: graph and game tree search problems - n-queens,sliding puzzle, sudoku, minimal sudoku, jeep problem, knapsack problem, traveling salesman problem,prisonner's dilemma, iterated prisonner's dilemma, pattern recognition / classification, imitation game(Turing's test), artificial life and cellular automata, Conway's game of life. Minsky's views on AI.

2

Introduction to Artificial Intelligence

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T-W-2Graph search algorithms: Breadth-First-Search, Best-First-Search, A*, Dijkstra's algorithm. Notion ofheuristics. Efficient data structures for implementations of above algorithms: hash map, priority queue(heap).

2

T-W-3 Algorithms for two-person games of perfect information: MIN-MAX, alpha-beta pruning, and theircomputational complexity. Horizon effect. 2

T-W-4Data classification (binary, linear) using the simple perceptron (Rosenblatt's perceptron).Forward pass. Learning algorithm. Linear separability of data. Novikoff's theorem on learningconvergence (with the proof).

2

T-W-5Multi-Layer-Perceptron (MLP) artificial neural network. Sigmoid as activation function. On-line vs off-linelearning. Derivation of the back-propagation algorithm. Possible variants. Overfitting and complexityselection for MLP via testing or cross-validation.

3

T-W-6

Genetic algorithms for optimization problems. Scheme of main genetic loop. Fitness function. Selectionmethods in GAs: roulette selection, rank selection, tournaments. "Exploration vs. exploitation"problem. Remarks on convergence, premature convergence (population diversity). Crossing-overmethods: one-point, two-points, multiple-point crossing-over. Mutation andits role in GAs (discrete andcontinuous). Examples of problems: knapsack problem, TSP. Exact solution of knapsack problem viadynamic programming.

2

T-W-7 Exam. 2

Student workload - forms of activity Number of hoursProgramming the sliding puzzle solver in Java. Preparation for the short test on searching graphs. 8A-L-1

Programming an AI for the connect4 game. Preparation for the short test on searching game trees. 8A-L-2Programming a complexity selection method for MLP via cross-validation. Preparation for a short teston MLP. 4A-L-3

Programming a comparison of GAs vs dynamic programming approach for the knapsack problem.Preparation for a short test on GAs. 4A-L-4

Participation in lab classes. 16A-L-5Getting familiar with pdf materials on non-linear classification by means of the kernel trick (Gaussiankernels + Rosenblatt's perceptron). 4A-L-6

Getting familiar with SaC Java library and its documentation. 16A-L-7


Consultations with lecturer. 2A-W-2

Self-preparation for the exam. 18A-W-3



M-2 Case study method.

M-3 Didactic games.


M-5 Demonstration.

Evaluation methods (F - progressive, P - final)S-1 Five short tests (10 minutes long) at the end of each topic during the lab.F

S-2 Five grades for the programs written as homeworks.F


P

S-4 Final grade for lectures from the test (1.5 h).P



study





education

Teachingmethods


methods

KnowledgeWM-WI_1-_??_W01Student has an elementary knowledge on AI problems andalgorithmic techniques applicable to solve them.

SkillsWM-WI_1-_??_U01Student can design and implement elementary AI algorithms.


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3,03,54,04,55,0


Required reading1. S. Russel, P. Norvig, Introduction to Artificial Intelligence, A Modern Approach, Prentice Hall, 2010, 3rd edition

Supplementary reading1. P. Klęsk, Electronic materials available at: http://wikizmsi.zut.edu.pl, 2015

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Field of study

Mode of study


ECTS


Code

Electives

Language

Elective group


stationary

WI-1-JSW

3,0

credits english

ECTS (forms) 3,0

Level first cycle

Area(s) of study


Module

Course unit Introduction to JavaScript web applicationdevelopment






Małachowski Bartłomiej ([email protected])Leading teacher

Other teachers

PrerequisitesW-1 Basic knowledge on procedural and object-oriented programming.

W-2 Good knowledge on HTML and CSS languages

Module/course unit objectivesC-1 To be able to independently develop a simple Single Page Application in JavaScript with REST data-exchenge capabilities.

Course content divided into various forms of instruction Number of hoursT-L-1 Basic JS programming - control statemets, loops, functions, objects and prototypes 4

T-L-2 Modification and dynamic building of documents with DOM API 4

T-L-3 Using several basic web APIs - geolocation, canvas, media etc. 4

T-L-4 Async HTTP requests with XMLHttpRequest API, JSON - serialization and parsing 4

T-L-5 Implementation of simple REST webservice client 6

T-L-6 Development of example Single Page Application, integration of external APIs (ex. Google Maps, Socialmedia services etc.) 8

T-W-1 Principles of JavaScript programming 2

T-W-2 Document Object Model API 2

T-W-3 Geo Location API and other pupular Web Javascript APIs 2

T-W-4 Asynchronous HTTP requests with XMLHttpRequest API 2

T-W-5 Development tools: dependency management, building and deployment 1

T-W-6 Principles of RESTfull web services and JSON data format 2

T-W-7 Single page applications - principles and development 4

Student workload - forms of activity Number of hoursIndividual work with given tasks 20A-L-1

participation in classes 30A-L-2

Preparations to classes and reading 10A-L-3


Self study 15A-W-2

Exam 1A-W-3

Teaching methods / toolsM-1 Auditorial lectures

M-2 Individual work - programming taks

Introduction to JavaScript web application development

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Evaluation methods (F - progressive, P - final)S-1 Evalution of developed programming tasks through code review made by the teacherF

S-2 Final examP



study





education

Teachingmethods


methods

Knowledge

Skills



Knowledge

Skills


[ logo uczelni ]

Field of study

Mode of study


ECTS


Code

Electives

Language

Elective group


stationary

WI-1-IMP

6,0

credits english

ECTS (forms) 6,0

Level first cycle

Area(s) of study


Module

Course unit Intro to Mathematical Programming









Module/course unit objectivesC-1 The course introduces to techniques for solving optimization tasks based on mathematical programming methods

Course content divided into various forms of instruction Number of hoursT-L-1 Linear programming: geometric method 4

T-L-2 Linear programming: simplex algorithm 4

T-L-3 Transportation theory: transport task 6

T-L-4 Program Evaluation and Review Technique (PERT) 5

T-L-5 Critical Path Method (CPM) 5

T-L-6 Traveling salesman problem: computing a solution 5

T-L-7 Exam 1

T-W-1 Intro to linear programming 7

T-W-2 Applications of linear programming 2

T-W-3 Intro to transportation theory 6

T-W-4 Applications of transportation theory 2

T-W-5 Intro to network Programming 6

T-W-6 Applications of network programming 2

T-W-7 Traveling salesman problem 4

T-W-8 Exam 1






M-2 Discussion



Intro to Mathematical Programming

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S-2 Written examP



study





education

Teachingmethods


methods

Knowledge

C-1 S-2


M-1M-2

WM_1-_null_W01After the lectures the student will be able to define and descrbe:-linear programming methods and problems,-transportation task methods and problems,-network programming methods and problems,-traveling salesman problem.

T-W-5T-W-6T-W-7

Skills

C-1 S-1T-L-1T-L-2T-L-3

M-2M-3

WM_1-_null_U01The student will be able to use the methods which will bepresented on the laboratories

T-L-4T-L-5T-L-6




3,0 The student has to define and describe methods and problems presented on the lectures

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3,0 The student is able to use the methods which were presented on the laboratories

3,54,04,55,0



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Field of study

Mode of study


ECTS


Code

Electives

Language

Elective group


stationary

WI-1-IST

6,0

credits english

ECTS (forms) 6,0

Level first cycle

Area(s) of study


Module

Course unit Intro to Statistic: Making Decisions Based on Data







Other teachers



C-1 The course introduces to techniques for visualizing relationships in data and systematic techniques for understanding therelationships using mathematics.


T-L-1 Visualizing relationships in data (seeing relationships in data and predicting based on them,simpson's paradox, etc.) 4

T-L-2 Probability (Bayes Rule, correlation vs. causation, etc.) 5

T-L-3 Estimation (maximum likelihood estimation, mean, median, mode, standard deviation, variance, etc.) 5

T-L-4 Outliers and normal distribution (outliers, quartiles, binomial distribution, central limit theorem,manipulating normal distribution, etc.) 5

T-L-5 Inference (confidence intervals, hypothesis testing, etc.) 5

T-L-6 Regression (linear regression, correlation, etc.) 5

T-L-7 Exam 1

T-W-1 Visualizing relationships in data (seeing relationships in data and predicting based on them, simpson'sparadox, etc.) 4

T-W-2 Probability (Bayes Rule, correlation vs. casuation, etc.) 5

T-W-3 Estimation (maximum likelihood estimation, mean, median, mode, standard deviation, variance, etc.) 5

T-W-4 Outliers and normal distribution (outliers, quartiles, binomial distribution, central limit theorem,manipulating normal distribution, etc.) 5

T-W-5 Inference (confidence intervals, hyphotesis testing, etc.) 5

T-W-6 Regression (linear regression, correlation, etc.) 5

T-W-7 Exam 1

Student workload - forms of activity Number of hoursThe attendance in the lectures 30A-L-1





M-2 Discussion


Intro to Statistic: Making Decisions Based on Data

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S-2 Written examP



study





education

Teachingmethods


methods

Knowledge

C-1 S-2T-L-1T-L-2T-L-3

M-1M-2

WM-WI_1-_null_W01After the lectures the student will be able to define and describepresented statistical techniques and measures

T-L-4T-L-5T-L-6

Skills

C-1 S-1M-2M-3

WM-WI_1-_null_U01The student will be able to calculate and use the main statisticalmeasures and techniques




3,0 The student is able to define and describe the main statsistical measures and techniques

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3,0 The student is able to calculate and use statistical measures and techniques

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Field of study

Mode of study


ECTS


Code

Electives

Language

Elective group


stationary

WI-1-JAV

6,0

credits english

ECTS (forms) 6,0

Level first cycle

Area(s) of study


Module

Course unit Java programming






Wierciński Tomasz ([email protected])Leading teacher

Other teachers

PrerequisitesW-1 Programming basics

W-2 Object programming

Module/course unit objectivesC-1 Familiar with the syntax and structures of the Java language

C-2 Knows how to analyze and implement source code in Java language

C-3 Understands the need for further development of professional skills in the field of Java language

Course content divided into various forms of instruction Number of hoursT-L-1 Introduction 1

T-L-2 Data types and objects 2

T-L-3 Operators 2

T-L-4 Control instructions 2

T-L-5 Packages 2

T-L-6 Exceptions 2

T-L-7 Encapsulation, inheritance, polymorphism 5

T-L-8 Abstract classes, interfaces 4

T-L-9 Parametrized types 2


T-L-11 Threads 2

T-L-12 GUI programming 4

T-W-1 Data types and objects 1

T-W-2 Operators 1

T-W-3 Control instructions 1

T-W-4 Packages 1

T-W-5 Exceptions 1

T-W-6 Encapsulation, inheritance, polymorphism 3

T-W-7 Abstract classes, interfaces 2

T-W-8 Parametrized types 1

T-W-9 Input-output operations 1

T-W-10 Threads 1

T-W-11 GUI programming 2

Java programming

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Student workload - forms of activity Number of hoursParticipation in class 30A-L-1

Preparing to perform exercises 30A-L-2

Self-study of the literature 30A-L-3


Participation in class 15A-W-1

Self-study of the literature 15A-W-2

Exam preparation 16A-W-3

Participation in consultations 10A-W-4

Participation in exam 3A-W-5


M-2 Multimedia presentation

M-3 Laboratory


S-2 Project workF



study





education

Teachingmethods


methods

Knowledge

C-2 S-1


M-1M-2

WM-WI_1-_null_W01Knows how to analyze and implement source code in Javalanguage

T-W-7T-W-8T-W-9T-W-10T-W-11

Skills

C-1 S-2


M-3WM-WI_1-_null_U01Familiar with the syntax and structures of the Java language

T-L-7T-L-8T-L-9T-L-10T-L-11T-L-12


C-3 S-1S-2

T-L-1T-L-2T-L-3T-L-4T-L-5T-L-6T-L-7T-L-8T-L-9T-L-10T-L-11T-L-12

M-1M-2M-3

WM-WI_1-_null_K01Understands the need for further development of professionalskills in the field of Java language

T-W-1T-W-2T-W-3T-W-4T-W-5T-W-6T-W-7T-W-8T-W-9T-W-10T-W-11



3,0 The student knows the rules and syntax of Java language

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3,0 The student is able to implement a source code in Java language according to the knowledge they gained in the class

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Other social / personal competencesWM-WI_1-_null_K01 2,0

3,0 The student understands the need to learn java.

3,54,04,55,0

Required reading1. Bruce Eckel, Thinking in Java (4th Edition), Prentice Hall, 2006

Supplementary reading1. Poornachandra Sarang, Java Programming (Oracle Press), McGraw-Hill Osborne Media, 2012, 1

2. Java, A Beginner's Guide, 5th Edition, McGraw-Hill Osborne Media, 2011, 5

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Field of study

Mode of study


ECTS


Code

Electives

Language

Elective group


stationary

WI-1-KEO

4,0

credits english

ECTS (forms) 4,0

Level first cycle

Area(s) of study


Module

Course unit Knowledge Engineering and Ontology Development







Other teachers


Module/course unit objectivesC-1 Familiar with the syntax, structures and principles used in OWL language

C-2 The ability to design and write small-scale ontologies and to use reasoning mechanisms

Course content divided into various forms of instruction Number of hoursT-L-1 Introduction to the ontologies 2

T-L-2 Protégé ontology editor and OWL language 3

T-L-3 Building an OWL ontology: defining class hierarchy 4

T-L-4 OWL object property characteristics 2

T-L-5 Building an OWL ontology: defining individuals and data type properties 4

T-L-6 Graphical visualization of the ontology 2

T-L-7 Describing and defining classes 3

T-L-8 The application of reasoning mechanisms and query tools 5

T-L-9 The application of plugins and tools to manage the ontology 5

T-W-1 Introduction to the ontologies 2

T-W-2 Ontology editors and standards for ontology description 3

T-W-3 Selected approaches to the ontology construction and knowledge engineering methods 2

T-W-4 Building an OWL ontology 6

T-W-5 Primitive and defined classes 4

T-W-6 Selected reasoning mechanisms and Open World Reasoning 4

T-W-7 Reusing of existing ontologies 2

T-W-8 Creating other OWL constructs in Protégé 2

T-W-9 Restriction types 2

T-W-10 Ontology-based solutions to knowledge extraction 2

T-W-11 Exam 1





Knowledge Engineering and Ontology Development

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M-2 Discussion



S-2 Project workF



study





education

Teachingmethods


methods

KnowledgeWM-WI_1-_??_W01After the course the student should be able to understand anduse the basic ontology constructs in OWLWM-WI_1-_??_W02After the course the student should be able to design andconstruct a small-scale ontology

Skills




3,03,54,04,55,0

WM-WI_1-_??_W02 2,03,03,54,04,55,0

Skills


Required reading1. Michael K. Smith, Chris Welty, and Deborah L. McGuinness, OWL Web Ontology Language Guide, 2004, http://www.w3.org/TR/owl-guide/1. Matthew Horridge (eds.), A Practical Guide To Building OWL Ontologies Using Protege 4 and CO-ODE Tools Edition 1.2, The Universityof Manchester, Manchester, 20092. Protege tutorial. Available from http://protege.stanford.edu/

Supplementary reading2. Natalya F. Noy and Deborah L. McGuinness, Ontology Development 101: A Guide to Creating Your First Ontology, Stanford KnowledgeSystems Laboratory Technical Report, 2001

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Field of study

Mode of study


ECTS


Code

Electives

Language

Elective group


stationary

WI-1-KED

3,0

credits english

ECTS (forms) 3,0

Level first cycle

Area(s) of study


Module

Course unit Knowledge extraction from data with rough setmethod and its applications







Korzeń Marcin ([email protected]), Sałabun Wojciech ([email protected])Other teachers

PrerequisitesW-1 Knowledge of basics of high mathematics


C-1 Acquirement of competence and practice of knowledge extraction in form of rule basis from information tables about asystem of interest.

Course content divided into various forms of instruction Number of hoursT-L-1 Exercises in various methods of attribute discretization. 2

T-L-2Identification of elementary conditional and decisional sets (concepts) from the informational table of asystem. Visualization of conditional and decisional sets. Decomposition of decisional sets in conditionalones.

2

T-L-3 Determining of absolute and relative attribute reducts, minimal sets of attributes and attribute cores. 2

T-L-4 Determining rough models of systems in form of rules' basis. Rules' reduction and verification. 3

T-L-5 Calculating quality measures of rough set models, determining of possible risk due to attributereduction. 2

T-L-6 Determining soft rough set models of systems, soft attribute reduction, generating and processingprobabilistic rules. 2

T-L-7 Constructing the rough set model for a given system as finishing of laboratory exercices. 2

T-W-1 Example of a real problem solved with use of rough sets, 1

T-W-2 Discretization of variables in problems, its meaning and usefulness. Basic ways of discretization. 1

T-W-3 Basic notions of rough sets. 1

T-W-4 Absolute and relative reduction of redundant system attributes. 2

T-W-5 Quality measures of rough set models. 1

T-W-6 Generating of certain and uncertain information rules about the system, their reduction andprocessing. 2

T-W-7 Rules' risk occuring due to reduction of conditional attributes. 1

T-W-8 "Soft" version of rough sets enabling generating both certain and uncertain (probabilistic) rules and"soft" attribute reduction. 4

T-W-9 Example of rough set application showing successive realization steps necessary to correct extractionof rule basis with use of rough sets. 2

Student workload - forms of activity Number of hoursParticipation in laboratory excercises 15A-L-1

Consultations referring to laboratory excerces 10A-L-2Elaborating of the project of an own rough set model of a real system for testing student competencein knowledge extraction from information tables of systems 35A-L-3

Participating in consultaions 3A-W-1

Knowledge extraction from data with rough set method and its applications

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Student workload - forms of activity Number of hoursStudying of lecture texts and of the recommended literature 12A-W-2


Teaching methods / toolsM-1 Information lecture with presentation

M-2 Laboratory excercises in individual solving of sub-problems given by an academician and realization of the end-projectsummarizing lectures and laboratory


S-1 Lectures: summarizing evaluation of the student on the basis of the end-project of knowledge extraction with roughsets individually realized by the student with taking into account student activity during lectures.P

S-2 Laboratory: forming evaluation of the student based on the student activity duaring laboratory trainingF



study





education

Teachingmethods


methods

Knowledge

C-1 S-1


M-1WM-WI_1-_??_W01The student has knowledge about rough sets, models createdon the base of them, and main applications of rough sets.


Skills

C-1 S-2T-L-1T-L-2T-L-3T-L-4

M-2WM-WI_1-_??_U01The student has the ability to create rough set models in form ofrules.

T-L-5T-L-6T-L-7




3,0 The student has the basic knowledge about rough sets and rough set models.

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3,0 The student has the basic practical ability in creating of rough set models.

3,54,04,55,0


Required reading1. Lech Polkowski, Rough sets. Mathematical foundations., Physica-Verlag, A Springer-Verlag Company, Heilderberg, New York,, 2002, 1

2. W. Pedrycz, A. Skowron, V. Kreinowich (editors), Handbook of granular computing, Wiley, Chichester, England, 2008, 1

Supplementary reading1. S.K. Pal, L. Polkowski, A. Skowron (editors), Rough-Neural Computing. Techniques for Computing with Words, Springer, BerlinHeidelberg, New York, 2004, 1

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Field of study

Mode of study


ECTS


Code

Electives

Language

Elective group


stationary

WI-1-LAT

2,0

credits english

ECTS (forms) 2,0

Level first cycle

Area(s) of study


Module

Course unit LaTeX – document preparation system for engineers







Other teachers

PrerequisitesW-1 Ability to use a computer running Linux or MS Windows operating system.

Module/course unit objectivesC-1 Practical skills in typesetting of engineering documents using LaTeX system.


T-L-1

Preparing of documents of increasing complexity; changing of the font type and size, defining of thetext layout, tables, complex mathematical formulas and mathematical texts; creating and insertingpictures; analysis of style files and preparation own styles for journals, books, reports and thesis;merging results of all exercises in a single document with the form of a book, with table of contents,bibliography, appendices and index.

15

T-W-1

Description of the installation and initialization of the package, setting of environment variables,hyphenation file. LaTeX input file and the principles of its building, permanent elements of the file.Structure of the document: the division of the document into parts, chapters, sections, paragraphs,etc., title page, the main file and included files, creating of a table of contents, table of figures andtables, attaching a bibliography, creating an index, references to the labels, usage of the counters.Defining own classes of documents: building of the style definition file and possibilities of changing itscontent. Defining of running heads for page headings and footers, defining of parameters for lists,floating objects, defining of headers for chapter and subsections, changing of the format of the table ofcontents and bibliography. Predefined classes of document and format, format definition file declaredin the preamble (page size, the type of numbering, margins, running head, footer). Defining the typeand size of fonts, special characters, accents, Polish diacritic characters. Length measures, horizontaland vertical spacing, references, breaking lines and pages. Defining of indivisible elements. Multiplecolumns usage. Greek and Cyrillic alphabet. Mathematical texts: mathematical environment, usingmathematical expressions and symbols (indices, fractions, roots, equations and their systems,matrices, complex formulas), spacing and bold in math mode. Special text structures: definingminipages, lists and tables, creating pictures and including them into document, language ofgeometric figures definition. Changes to the definitions, creating of own definitions and defining a newenvironment. Creating new variable objects. Correction of the errors: error messages and warnings inLaTeX and TeX, error correction capabilities.

15

Student workload - forms of activity Number of hoursAttendance in the classes. 15A-L-1

Preparation for the classes 7*1 h. 7A-L-2

Individual completing of the document. 10A-L-3



Exam 2A-W-3

Consultations 3A-W-4


LaTeX ? document preparation system for engineers

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M-2 Laboratory work - individual preparation of the document with increasing complexity

Evaluation methods (F - progressive, P - final)S-1 Lecture - oral examP

S-2 Laboratory work - evaluation of submitted document that has been prepared during the courseP



study





education

Teachingmethods


methods

Knowledge

C-1 S-1T-W-1

M-1WM_1-_null_W01Student has knowledge about typesetting engineeringdocuments with LaTeX system

Skills

C-1 S-2T-L-1

M-2WM_1-_null_U01Student has practical skills in typesetting of engineeringdocuments with LaTeX system




3,0 Student has knowledge about basic techniques of typesetting in LaTeX: uncomplicated logical structure of a document, onedocument class, one environment.

3,54,04,55,0


3,0 Student has practical skills in typesetting of engineering documents in LaTeX using basic techniques: uncomplicated logicalstructure of a document, one document class, one environment.

3,54,04,55,0


Required reading1. L. Lamport, LaTeX: A Document Preparation System, Addison-Wesley, Boston, 1994

2. F. Mittelbach et al., The LaTeX Companion (Tools and Techniques for Computer Typesetting), Addison-Wesley, Boston, 2004

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Field of study

Mode of study


ECTS


Code

Electives

Language

Elective group


stationary

WI-1-MBV

3,0

credits english

ECTS (forms) 3,0

Level first cycle

Area(s) of study


Module

Course unit Management and Business CommunicationVirtualisation





Sulikowski Piotr ([email protected])Leading teacher

Sulikowski Piotr ([email protected])Other teachers

Prerequisites

W-1 General knowledge of communication principles. Thorough knowledge in the fields of organization and management. ITbackground required.

Module/course unit objectivesC-1 To improve understanding of management and communication, with particular focus on virtual organizations.

Course content divided into various forms of instruction Number of hoursT-W-1 Introduction to Management and Communication. 6

T-W-2 Virtual Organizations - Genesis. 6

T-W-3 Virtual Organizations - Creation and Characteristics. 6

T-W-4 Information Systems Engineering in Virtual Organizations. 6

T-W-5 Trust Management. 6

Student workload - forms of activity Number of hoursattending lectures 30A-W-1

homework 30A-W-2

preparation for exam 15A-W-3

consultation 12A-W-4

exam 3A-W-5

Teaching methods / toolsM-1 lectures

Evaluation methods (F - progressive, P - final)S-1 continuous assessmentF

S-2 written/oral examP



study





education

Teachingmethods


methods

KnowledgeWM-WI_1-_??_W01Student understands communication principles.

SkillsWM-WI_1-_??_U01Student can apply the principles to management.


Management and Business Communication Virtualisation

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WM-WI_1-_??_K01Student values the importance of communication in business.



3,0 Students understands the principles on a basic level.

3,54,04,55,0


3,0 Student can apply the principles on a basic level.

3,54,04,55,0


3,0 Student proves that they value the importance of communication in most business situations.

3,54,04,55,0

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Field of study

Mode of study


ECTS


Code

Electives

Language

Elective group


stationary

WI-1-MAT

6,0

credits english

ECTS (forms) 6,0

Level first cycle

Area(s) of study


Module

Course unit MATLAB Programming







Other teachers


Module/course unit objectivesC-1 Acquire the basic knowledge on Matlab programming

C-2 The practical skills of the Matlab programming

Course content divided into various forms of instruction Number of hoursT-L-1 Practical exercises of program content 29

T-L-2 Exam 1

T-W-1 Create Scripts 6

T-W-2 Create Live Scripts 2

T-W-3 Loop Control Statements 6

T-W-4 Conditional Statements 6

T-W-5 Add Comments to Programs 2

T-W-6 Run Code Sections 2

T-W-7 Run Sections in Live Scripts 3

T-W-8 Scripts vs. Functions 2

T-W-9 Exam 1






M-2 Discussion



S-2 Written examP

MATLAB Programming

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study





education

Teachingmethods


methods

Knowledge

C-1 S-2T-W-1T-W-2T-W-3T-W-4

M-1M-2

WM-WI_1-_null_W01After the lectures the student will be able to define and describeconcepts of Scripts, Live Scripts, Loop Control Statements,Conditional Statements


Skills

C-2 S-1T-L-1 M-2

M-3WM-WI_1-_null_U01The student will be able to write a simple program in the Matlabenviorment




3,0 The student is able to define and describe the basic concepts of the Matlab programming

3,54,04,55,0


3,0 The student is able to write a simple program in the Matlab enviorment

3,54,04,55,0



[ logo uczelni ]

Field of study

Mode of study


ECTS


Code

Electives

Language

Elective group


stationary

WI-1-MDS

5,0

credits english

ECTS (forms) 5,0

Level first cycle

Area(s) of study


Module

Course unit Microprocessor design and soft-processors








Other teachers

PrerequisitesW-1 Basics of Eelctronics.

W-2 Digital Design.

Module/course unit objectivesC-1 Designing of PCB with processor on board.

C-2 Processor internal structure and soft processor creation on FPGA.

C-3 General knowledge about processor internal structure.

Course content divided into various forms of instruction Number of hoursT-L-1 Soft processor structure of example processor in details. 6

T-L-2 Processor PCB design. 4

T-L-3 Processor programming 5

T-P-1 Implementation of a chosen processor or a processor system. 15

T-W-1 Different implementations of the ALU from inside. 2

T-W-2 Synthesis of a control unit 2

T-W-3 Low power technologies – methodologies, its advantages and pitfalls. 1

T-W-4 Processor example in Verilog. 5

T-W-5 Hardware description languages. 2

T-W-6 Dynamic instruction set processors and processors with dynamic structure. 1

T-W-7 PCB design for processor. 2

Student workload - forms of activity Number of hoursLaboratories 15A-L-1

Individual work 15A-L-2

Project 60A-P-1

Lectures 15A-W-1



M-2 Laboratories.

M-3 Project.

Microprocessor design and soft-processors

[ logo uczelni ]

Evaluation methods (F - progressive, P - final)S-1 Final exam.P

S-2 Laboratory reports.F

S-3 Project.P



study





education

Teachingmethods


methods

KnowledgeWM-WI_1-_??_W01Student knows in details basic processor structures and cantailor it to the project.

SkillsWM-WI_1-_??_U01Student can design (hardware and software) a processorsystem.WM-WI_1-_??_U02Student can create custom soft-processor in Verilog/VHDL onFPGA device.




3,03,54,04,55,0


3,03,54,04,55,0

WM-WI_1-_??_U02 2,03,03,54,04,55,0


Required reading1. P. Ienne, R. Leupers, Customizable Embedded Processors: Design Technologies and applications, Morgan Kaufmann

2. J. Nurmi, Processor Design: System-On-Chip Computing for ASICs and FPGAs, Springer, 2007

3. D. Liu, Embedded DSP Processor Design, Volume 2: Application Specific Instruction Set Processors, Morgan Kaufmann, 2008

Supplementary reading1. J. Stokes, Inside the Machine, No Starch Press

[ logo uczelni ]

Field of study

Mode of study


ECTS


Code

Electives

Language

Elective group


stationary

WI-1-MAD

4,0

credits english

ECTS (forms) 4,0

Level first cycle

Area(s) of study


Module

Course unit Mobile Application Development







Maciaszczyk Radosław ([email protected])Leading teacher

Maciaszczyk Radosław ([email protected])Other teachers

PrerequisitesW-1 Knowledge of at least one object programming language, Preferred Java language

Module/course unit objectivesC-1 The main objective of the course is to intorduction to Androis OS

C-2 Students will be prepared to create applications for mobile devices with Android OS

Course content divided into various forms of instruction Number of hoursT-L-1 Introduction to Android 2

T-L-2 Application Fundamentals 2

T-L-3 User Interface 2

T-L-4 Sensors and Location 2

T-L-5 Data Storage 4

T-L-6 Connectivity 2

T-L-7 Camera and audio 1

T-P-1 Introduction to project 2

T-P-2 Project 10

T-P-3 Documentation 2

T-P-4 Presentation procjet 1

T-W-1 Introducing to mobile device. 1

T-W-2 The History of Android 1

T-W-3 Application Fundamentals 2

T-W-4 Activity lifecycle 1

T-W-5 User Interface 2

T-W-6 Sensors 2

T-W-7 Threads and Services 1

T-W-8 Storing and retrieving data 1

T-W-9 Networking 1

T-W-10 Multimedia 2

T-W-11 Location Services. 1


Mobile Application Development

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Student workload - forms of activity Number of hoursPreparation for the classes. 15A-L-2

Preparation for the classes. 45A-P-1

Participation in classes. 15A-P-2



Teaching methods / toolsM-1 Lectures: informative, problem solving, conversational.









study





education

Teachingmethods


methods

Knowledge

Skills



Knowledge

Skills


Required reading1. Ian F. Darwin, Android Cookbook, Problems and Solutions for Android Developers, O'Reilly, 20122. Zigurd Mednieks, Laird Dornin, G. Blake Meike, Masumi Nakamura, Programming Android, 2nd Edition-Java Programming for the NewGeneration of Mobile Devices, O'Reilly, 2012

Supplementary reading1. Mark L. Murphy, The Busy Coder's Guide to Android Development, CommonsWare – Digital version -http://commonsware.com/Android/, 20142. http://d.android.com, 2011

[ logo uczelni ]

Field of study

Mode of study


ECTS


Code

Electives

Language

Elective group


stationary

WI-1-PAP

6,0

credits english

ECTS (forms) 6,0

Level first cycle

Area(s) of study


Module

Course unit Parallel Programming






Bielecki Włodzimierz ([email protected])Leading teacher

Other teachers

PrerequisitesW-1 Compilers 1 You are expected to have some basic programming skills using C or C++.


C-1

To develop an understanding of major parallel programming modelsTo be able to identify promising applications of parallel computingTo be able to develop typical parallel algorithms and implement prototype parallel programs using API OpenMPTo be able to analyze the performance of parallel programs

Course content divided into various forms of instruction Number of hoursT-L-1 Pragma parallel 4

T-L-2 Pragma For 4

T-L-3 Pragma Sections 4

T-L-4 Pragma Single 2

T-L-5 Pragma Critical 2

T-L-6 Coding an algorithm in OpenMP 6

T-L-7 Evaluating speed-up of an OpenMP program 4

T-L-8 Applying Amhdal's and Gustafson's laws 4

T-W-1 Introduction: From serial to parallel thinking. A history of parallel computers and lessons learnedfrom them. 2

T-W-2 Dependences in programs 2

T-W-3 Basic loop transformations 2

Parallel Programming

[ logo uczelni ]


T-W-4

API OpenMP, version 2.

Processes and threadsFork-Join modelWhat does OpenMP stand for?Limitations of OpenMPOpenMP Directive ResponsibilitySynchronization in OpenMPPragma Parallel and its clausesWhat is a structured blockControl of the number of threads in a parallel regionDynamic threadsNested parallel regionsParallel directive restrictionsPrivate, firstprivate, shared, and default clausesPurpose of the DO / for directive and its restrictionsOrdered clauseLast private clauseSchedule clauseReduction clauseNowait clauseDefault scoping rules in OpenMPExceptions to the rule that unscoped variables are made shared by default.Removing anti dependencesRemoving output dependencesRemoving data flow dependencesTREADPRIVATE clauseCOPYIN clausePragma SECTIONS and its clausesRestrictions of pragma SectionsPragma single, its clauses and restrictionsCombined constructsRestrictions of work-sharing constructsOrphan directivesScopes in an orphan constructionNested parallelismEnvironment variablesRun-Time Library RoutinesNeed for synchronizationCRITICAL directive and its restrictionsAtomic directive, its restrictionUsing the lock routines to implement a critical sectionBARRIER directive, its restrictionsORDERED directive, its restrictionsMASTER directive, its restrictionsFLUSH directive, its restrictions

16

T-W-5

Parallel Program Performance metrics.

Key factors impacting performanceCashes and LocalityLocality and SchedulesFalse sharingInconsistent parallelizationHow barriers impact performanceHow critical sections impact performanceGood Practice improving performance

Deterministic programProgram granularityProgram localityHow caches workProgram speed-upProgram efficiencyAMDAHL’S LAWGUSTAFSON’S LAW

4

T-W-6 Parallel algorithm design 2

T-W-7 Performance models 2

Student workload - forms of activity Number of hoursparticipation in laboratories 30A-L-1

Participation in consultations 10 10A-L-2

preparation for laboratories 50A-L-3

Lectures 30A-W-1

Preparing to examination 50A-W-2

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Student workload - forms of activity Number of hoursExamination 2 2A-W-3


Teaching methods / toolsM-1 Informative / conversational lectures


M-3 the Final exam by checking the learning outcomes: presenting questions and assessing the answers

Evaluation methods (F - progressive, P - final)S-1 Assessment of the degree of practical tasks at the end of each laboratoryF



study





education

Teachingmethods


methods

KnowledgeWM-WI_1-_??_W01The student has basic knowledge in the OpenMP standard.

SkillsWM-WI_1-_??_U01The student is able to write parallel programs in the OpenMPstandard.

Other social / personal competencesWM-WI_1-_??_K01The student is able to work with colleagues in a group.



3,03,54,04,55,0


3,03,54,04,55,0


3,03,54,04,55,0

Required reading1. Rohit Chandra Ramesh Menon Leo Dagum David Kohr Dror Maydan Jeff McDonald, Parallel Programming in OpenMP, MorganKaufmann, 20012. Thomas Rauber, Parallel Programming: for Multicore and Cluster Systems, Springer, 2010

Supplementary reading1. OpenMP reference: Specification of OpenMP 2.5 0 API for C/C++

[ logo uczelni ]

Field of study

Mode of study


ECTS


Code

Electives

Language

Elective group


stationary

WI-1-PD1

5,0

credits english

ECTS (forms) 5,0

Level first cycle

Area(s) of study


Module

Course unit Programmable control devices 1 – logic controlsystems






Jaszczak Sławomir ([email protected])Leading teacherJaszczak Sławomir ([email protected]), Sałabun Wojciech([email protected])Other teachers

PrerequisitesW-1 Physics - basics of the electricity

W-2 Electronics - basics of DC systems

W-3 Basic knowledge of the selected programming language (C/C++, Java, Python etc.)


C-1 General knowledge about : sensors and actuators , real time operation systems, logic functions, timers and counters,machine state syntesis in the Structured Text language.

C-2 Ability to syntesize logic functions, timers and counters, machine state syntesis in the Structured Text language.

Course content divided into various forms of instruction Number of hoursT-L-1 Basics of the ST programming 6

T-L-2 Syntesis of the logic functions 6

T-L-3 Syntesis of the state machine 12

T-L-4 Programming of the selected electrical motor - DC o AC 6

T-W-1 Introduction to programmable controllers 2

T-W-2 Sensors and actuators. 2

T-W-3 Real time operation systems 1

T-W-4 Basics of the Structured Text language. 1

T-W-5 Logic functions in the Structured Text language. 2

T-W-6 Timers and counters in the Structured Text language 2

T-W-7 Machine state syntesis in the Structured Text language. 2

T-W-8 Tips and tricks in the Structured Text language. 2

T-W-9 Exam 1


Self study of the literature 30A-L-2

Realization of the projects 45A-L-3


Self- study of the literature 15A-W-2

Preperation to an exam 15A-W-3


Programmable control devices 1 ? logic control systems

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Teaching methods / toolsM-1 Conversational lecture

M-2 Information lecture


Evaluation methods (F - progressive, P - final)S-1 Programming projectsF

S-2 Oral testF

S-3 Final project with oral testP



study





education

Teachingmethods


methods

Knowledge

C-1 S-2


M-1M-2

WM-WI_1-_null_W01General knowledge of the ST language syntax and ability oflogic functions and machines state synthesis.


Skills

C-2 S-1S-3

T-L-1T-L-2 M-3

WM-WI_1-_null_U01Ability of using general syntax of the ST language (logicfunctions, machines state, timers, counters, SET-RESETfunctions)

T-L-3T-L-4



KnowledgeWM-WI_1-_null_W01 2,0 A student isn't able to describe a logic function synthesis an its implementation in the ST language.

3,0 A student is able to describe a logic function synthesis an its implementation in the ST language.

3,5 A student is able to describe a logic function synthesis an its implementation in the ST language and also show and describesome practical examples.

4,0 A student is able to describe a logic function synthesis an its implementation in the ST language and also show and describesome practical examples. Additionally a student is able to explain TON and TOF timer's functions.

4,5A student is able to describe a logic function synthesis an its implementation in the ST language and also show and describesome practical examples. Additionally a student is able to explain TON and TOF timer's functions and show how to use themin a given logic function to make it more useful.

5,0A student is able to describe a logic function synthesis an its implementation in the ST language and also show and describesome practical examples. Additionally a student is able to explain TON and TOF timer's functions and show how to use themin a given logic function to make it more useful.A student can describe a machine state synthesis using the SFC languuage elements.

SkillsWM-WI_1-_null_U01 2,0 A student is not able to write a simple logic function using the ST language

3,0 A student is able to write and comment a simple logic function using the ST language

3,5 A student is able to write and comment a simple logic function using the ST language. Additionally a student is able tomodify an original function to have other functionality.

4,0A student is able to write and comment a simple logic function using the ST language. Additionally a student is able tomodify an original function to have other functionality. A student can freely use CASE and IF constructions in the STlanguage.

4,5A student is able to write and comment a simple logic function using the ST language. Additionally a student is able tomodify an original function to have other functionality. A student can freely use CASE and IF constructions in the ST languageand he is able to add additional conditions and modes to them.

5,0A student is able to write and comment a simple logic function using the ST language. Additionally a student is able tomodify an original function to have other functionality. A student can freely use CASE and IF constructions in the ST languageand he is able to add additional conditions and modes to them.A student is able to develope a state machine based on a description and elements of the ST language.


Required reading1. Kelvin T. Erickson, Programmable Logic Controllers, Dogwood Valley Press, 2016

2. B&R, Structured Text, B&R, 2017

[ logo uczelni ]

Field of study

Mode of study


ECTS


Code

Electives

Language

Elective group


stationary

WI-1-PD2

5,0

credits english

ECTS (forms) 5,0

Level first cycle

Area(s) of study


Module

Course unit Programmable control devices 2 – continuous controlsystems






Jaszczak Sławomir ([email protected])Leading teacherJaszczak Sławomir ([email protected]), Sałabun Wojciech([email protected])Other teachers

PrerequisitesW-1 Basic knowledge of the selected programming language (C/C++, Java, Python etc.)

W-2 Physics - a general knowledge of dynamical systems

Module/course unit objectivesC-1 General knowledge about feedback loop control structures and basic analog control algorithms (two state, PID etc.)

C-2Programming skills in structured text :Pre-processing of analog signalsSyntesis of the two state control algorithmSyntesis of the PID control algorith

Course content divided into various forms of instruction Number of hoursT-L-1 Pre-processing of the analog signals in the ST programming 6

T-L-2 Syntesis of the two state control algorithm 6

T-L-3 Syntesis of the PID control algorithm 12

T-L-4 Synthesis of the selected real time control system - speed or position control system 6

T-W-1 Introduction to the feedback loop control 4

T-W-2 Analog sensors and actuators. 4

T-W-3 Two state control algorithm. 2

T-W-4 PID control algorithm 4

T-W-5 Exam 1


Self study of the literature 30A-L-2

Realization of the projects 45A-L-3


Self- study of the literature 15A-W-2

Preperation to an exam 15A-W-3

Teaching methods / toolsM-1 Conversational lecture

M-2 Information lecture


Programmable control devices 2 ? continuous control systems

[ logo uczelni ]

Evaluation methods (F - progressive, P - final)S-1 Oral or the written testF

S-2 Programming projectsF

S-3 Final project with the oral testP



study





education

Teachingmethods


methods

Knowledge

C-1 S-1T-W-1T-W-2 M-1

M-2WM-WI_1-_null_W01General knowledge of the ST language syntax related to thefeedback loop control.

T-W-3T-W-4

Skills

C-2 S-2S-3

T-L-1T-L-2 M-3

WM-WI_1-_null_U01Ability of using general syntax of the ST language (PIDcontroller, types conversion, scaling-averaging-filteringfunctions)

T-L-3



KnowledgeWM-WI_1-_null_W01 2,0 A student isn't able to show a general structure of a feedback loop control system and describe its elements i.e. sensors,

actuators and control devices.

3,0 A student is able to show a general structure of a feedback loop control system and describe its elements i.e. sensors,actuators and control devices.

3,5A student is able to describe a PID control function and its implementation in the ST language.

A student is able to show a general structure of a feedback loop control system and describe its elements i.e. sensors,actuators and control devices.

4,0A student is able to describe a PID control function and its implementation in the ST language.A student is able to show a general structure of a feedback loop control system and describe its elements i.e. sensors,actuators and control devices. A student is also able to describe an analog-digital and digital - analog process.

4,5

A student is able to describe a PID control function and its implementation in the ST language.A student is also able to describe filtering and averaging functions to remove influence of the noise.A student is able to show a general structure of a feedback loop control system and describe its elements i.e. sensors,actuators and control devices. A student is also able to describe an analog-digital and digital - analog process.

5,0

A student is able to describe a PID control function and its implementation in the ST language. Additionally a student is ableto describe tuning methods of the PID control function.A student is also able to describe filtering and averaging functions to remove influence of the noise.A student is able to show a general structure of a feedback loop control system and describe its elements i.e. sensors,actuators and control devices. A student is also able to describe an analog-digital and digital - analog process.

SkillsWM-WI_1-_null_U01 2,0 A student is not able to write a simple program using the ST language and PID control function.

3,0 A student is able to write a simple program using the ST language and PID control function.

3,5 A student is able to write a simple program using the ST language and PID control function. Additionally a student is able totune PID control function manually.

4,0 A student is able to write a simple program using the ST language and PID control function. Additionally a student is able totune PID control function manually and can freely develope a simple HMI interface to enable online PID controller handling.

4,5 A student is able to write a simple program using the ST language and PID control function. Additionally a student is able totune PID control function manually and can freely develope a simple HMI interface to enable online PID controller handling.

5,0A student is able to write a simple program using the ST language and PID control function. Additionally a student is able totune PID control function manually and can freely develope a simple HMI interface to enable online PID controller handling.A student is also able to use filtering and averaging functions to remove influence of the noise.


Required reading1. Kelvin T. Erickson, Programmable Logic Controllers, Dogwood Valley Press, 2016

2. B&R, Structured Text, B&R, 2017

[ logo uczelni ]

Field of study

Mode of study


ECTS


Code

Electives

Language

Elective group


stationary

WI-1-PPA

4,0

credits english

ECTS (forms) 4,0

Level first cycle

Area(s) of study


Module

Course unit Prolog Programming for Artifcial Intelligence







Other teachers

PrerequisitesW-1 The course does not require any previous knowledge

Module/course unit objectivesC-1 Knowledge in Prolog programming and the ability to recognize different algorithms from Artificial Inteligence

C-2 Ability to implement some (search, reasoning, inductive programming, belief networks) AI algoritghm using Prologprogramming language

Course content divided into various forms of instruction Number of hoursT-L-1 Simple example - facts and rules 2

T-L-2 Declarative and procedural meaning 2

T-L-3 Operators and arithmetic 2

T-L-4 Lists in Prolog 4

T-L-5 Eight queens problem solution 2

T-L-6 Cut, negation and backtracking 2

T-L-7 Build in predicates 2

T-L-8 Debugging 2

T-L-9 Tree and graph representation and search 4

T-L-10 Expert systems (if then) 4

T-L-11 Minimax - game playing 4

T-W-1 From First predicate logic to Prolog 3

T-W-2 Prolog syntax, lists, operators, arithmetics 2

T-W-3 Backtracking and build in predicates 2

T-W-4 Program examples - search blind and informed 2

T-W-5 Expert systems in Prolog 3

T-W-6 Game playing 3


Homeworks 34A-L-2

Studing the literatrue 20A-L-3


Self studying literature 15A-W-2

Studying for test 6A-W-3

Prolog Programming for Artifcial Intelligence

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Teaching methods / toolsM-1 Lecture, presentation

M-2 Discussion, learning by doing

M-3 Software developing in Prolog

Evaluation methods (F - progressive, P - final)S-1 Short programming tasksF

S-2 Writing exam or quiz from knowledge representation and Prolog.P



study





education

Teachingmethods


methods

Knowledge

C-1 S-2T-W-1T-W-2T-W-3

M-1M-2

WM-WI_1-_null_W01Explain the logic programming paradigm. Understand theresoninig in Prolog. Represent knowledge in First Predicate Logicand Prolog syntax.

T-W-4T-W-5T-W-6

Skills

C-2 S-2


M-2M-3

WM-WI_1-_null_U01Develop a given algorithm in Prolog using build-in and ownpredicates. Debug the Prolog code. Describe how the result isobtained.

T-L-7T-L-8T-L-9T-L-10T-L-11




3,0 Basic knowladge in Predicate Logic and Prolog

3,54,04,55,0


3,0 Understanding examples from laboratories and implement them.

3,54,04,55,0


Required reading1. Ivan Bratko, Prolog programming for Artificial Intelligence, Pearson Education, 2001

[ logo uczelni ]

Field of study

Mode of study


ECTS


Code

Electives

Language

Elective group


stationary

WI-1-RRF

3,0

credits english

ECTS (forms) 3,0

Level first cycle

Area(s) of study


Module

Course unit Ruby on Rails framework for web development







Other teachers

PrerequisitesW-1 Ruby programming language

W-2 HTML and CSS basics

W-3 Databases basics

Module/course unit objectivesC-1 To understand how to use Ruby on Rails framework for dynamic web development.

C-2 To understand and apply Ruby on Rails framework paradgim to develop webpages with a relational database.

Course content divided into various forms of instruction Number of hoursT-L-1 A blog web application based on RoR. 3

T-L-2 A web application using scaffolding 3

T-L-3 Developing students’ idividaual projects. 9

T-W-1 Development environment. Installing vs virtual. Version controll GitHub, Bitbucket 3

T-W-2 Building application. MVC paradigm explaination, RoR rules. 2

T-W-3 User recognition. Log in and out. Styling 2

T-W-4 Working with Database in Rails 4

T-W-5 Using Scaffold 2

T-W-6 Adding scripts, pagination 2

Student workload - forms of activity Number of hoursParticipation in Labs 15A-L-1

Homeworks 18A-L-2

Individual projects 20A-L-3


Literatury study 15A-W-2

Prepareing for test 6A-W-3

Teaching methods / toolsM-1 Lectures with presentation

M-2 Laboratories developing webservices based on RoR

M-3 Discussion and learning by doing

Evaluation methods (F - progressive, P - final)S-1 Multiple choice quiz from RoR syntax.P

Ruby on Rails framework for web development

[ logo uczelni ]



study





education

Teachingmethods


methods

KnowledgeWM-WI_1-_??_W01The outcome of the course is knowledge and the ability to applyRoR framework for dynamic web development

SkillsWM-WI_1-_??_U01Construct/develop a webpage using RoR




3,03,54,04,55,0


3,03,54,04,55,0


Required reading1. Michael Hartl, Ruby on Rails Tutorial, https://www.railstutorial.org

Supplementary reading1. Online Ruby on Rails documentation, 2011, on-line

[ logo uczelni ]

Field of study

Mode of study


ECTS


Code

Electives

Language

Elective group


stationary

WI-1-RPL

3,0

credits english

ECTS (forms) 3,0

Level first cycle

Area(s) of study


Module

Course unit Ruby programming language







Other teachers

Prerequisites

Module/course unit objectivesC-1 Knowledge in syntax and semantics of Ruby programming language

C-2 An ability to use Ruby language to solve algorithmic problems

Course content divided into various forms of instruction Number of hoursT-L-1 Introduction to Ruby 3

T-L-2 Object programming in Ruby: Class, object, methods, module 4

T-L-3 Build in elements: strings, data, logic, predicates, arrays, lists 4

T-L-4 Files and operating system 2

T-L-5 An algorytminc task in Ruby 2

T-W-1 Ruby programming language intrudution, installing 2

T-W-2 Basic syntax and keywords, conditions, loops, variables 2

T-W-3 Objects, methods and local variables 2

T-W-4 Class and Module in Ruby 2

T-W-5 Object Self and its range 2

T-W-6 Dealing with errors and exceptions 1

T-W-7 Build in elements. 4

Student workload - forms of activity Number of hoursParticipation in Labs 15A-L-1

Homeworks 20A-L-2

Final task 18A-L-3


Literature reading, selfstuying 15A-W-2

Preparing to test 6A-W-3

Teaching methods / toolsM-1 Lectures with presentation and examples

M-2 Classes/laboratories - developing code in Ruby

Evaluation methods (F - progressive, P - final)S-1 Quiz from the Ruby syntaxP

S-2 Examination of progamming tasksF

Ruby programming language

[ logo uczelni ]



study





education

Teachingmethods


methods

Knowledge

C-1 S-1T-W-1T-W-2T-W-3T-W-4

M-1WM-WI_1-_null_W01Knowlage on Ruby programming language syntax. Objectoriented programming paradigm based on Ruby.

T-W-5T-W-6T-W-7

Skills

C-2 S-2T-L-1T-L-2T-L-3

M-2WM-WI_1-_null_U01Practical skills to use Ruby language to solve algorithmicproblems.

T-L-4T-L-5




3,0 Knowladge on basic sytnax and simple operations in Ruby

3,54,04,55,0


3,0 Understanding examples presented during classes.

3,54,04,55,0


Required reading1. Online Ruby documentation, 2011

2. David A. Black, The Well-Grounded Rubyis, Manning, 2014

[ logo uczelni ]

Field of study

Mode of study


ECTS


Code

Electives

Language

Elective group


stationary

WI-1-SEN

4,0

credits english

ECTS (forms) 4,0

Level first cycle

Area(s) of study


Module

Course unit Software engineering







Other teachers

PrerequisitesW-1 Basic knowledge and skills in object-oriented programming, relational databases.

Module/course unit objectivesC-1 Possess knowledge and obtain practical skills in developing main products of software engineering process.

C-2 Usage of techniques and tools for development process where outcomes from one stage flow to subsequent stages.


Course content divided into various forms of instruction Number of hoursT-L-1 Introduction to software engineering labs. Organisational issues. Preparing student environment. 2

T-L-2 Problem definition and introduction to requirements engineering. 2

T-L-3 Writing user and system specifications 4

T-L-4 User interface wireframing and design, processing design 2

T-L-5 Software analysis and modelling 6

T-L-6 Database design 2

T-L-7 Initial implementation of the prototype of the architecture 2

T-L-8 Completing student projects - documentation and implemntation 8

T-L-9 Project presentation and grading 2

T-W-1 Introduction to software engineering. 2

T-W-2 Gathering customer/user requirements. Writing user and system specifications. 2

T-W-3 Software analysis and modelling. Design patterns. 4

T-W-4 Software designing. Architectural patterns. Data design. User interface wireframing and design.Processing design. Prototyping. 2

T-W-5 Introduction to validation and verification. Software Testing. 4









Software engineering

[ logo uczelni ]

Student workload - forms of activity Number of hourspreparing for credit 13A-W-3



M-2 Lab exercises

M-3 Project






study





education

Teachingmethods


methods

Knowledge

C-1 S-3T-W-1T-W-2T-W-3

M-1WM-WI_1-_null_W02Describes main terms, processes and techniques used insoftware engineering.

T-W-4T-W-5T-W-6

Skills

C-1C-2C-3

S-1S-2S-3


M-2M-3

WM-WI_1-_null_U01Can create software project documentation with requirementsspecification, architectural design, and main test cases.



C-1C-3

S-1S-2S-3


M-1M-2M-3

WM-WI_1-_null_K01Ability to communicate with non-technical people




3,0 Student briefly describes main terms, majority of process elements and main techniques used in software engineering.

3,54,04,55,0


3,0 Student can use software tools to create software requirements specification with main elements correctly defined

3,54,04,55,0


3,0 Student can communicate with non-technical people to prepare and present requirements specification and selectedelements of software design

3,54,04,55,0

Required reading1. Bruegge B., Dutoit A.H., Object-Oriented Software Engineering Using UML, Patterns and Java, Prentice Hall, 2009, 3rd edition2. Larman C., Applying UML and Patterns: An Introduction to Object-Oriented Analysis and Design and Iterative Development, PrenticeHall, 2004, 3rd Edition

Supplementary reading1. Freeman E., Bates B., Sierra K., Robson E., Head First Design Patterns, O'Reilly Media, 2004

2. Wiegers K., Beatty J., Software Requirements, Microsoft Press, 2013, 3rd Edition

[ logo uczelni ]

[ logo uczelni ]

Field of study

Mode of study


ECTS


Code

Electives

Language

Elective group


stationary

WI-1-SEC

2,0

credits english

ECTS (forms) 2,0

Level first cycle

Area(s) of study


Module

Course unit Software for engineering calculations







Other teachers

PrerequisitesW-1 Basics of computer science.

Module/course unit objectivesC-1 Learning of main features and functions of Mathcad.

C-2 Learning of MATLAB and its programming language.


T-L-1 Mathcad - basics of the operation. Evaluation of values of mathematical expresions. Defining ofvariables and using them in calculations. 2

T-L-2 Creating of user functions. Creating of 2D and 3D plots. 2

T-L-3 Solving equations. Formatting of Mathcad sheets. 2

T-L-4 Performing of symbolic algebraic manipulations. 2

T-L-5 Filnal work in Mathcad. 2

T-L-6 Exercises in programming in MATLAB. Creating of matrices. Matrix operations. Matrix indexing. 2

T-L-7 Exercises in creating 2D and 3D plots. Creating and running scripts. 2

T-L-8 Exercises in creating and running functions. 4

T-L-9 Exercises in solving of simple numerical problems. 4

T-L-10 Simulink and selected MATLAB toolboxes. 3

T-L-11 Exercises in creating MATLAB programs with GUI (Graphic User Interface). 2

T-L-12 Final work in MATLAB. 3

T-W-1 Introduction to software for engineering calculation - overview of systems and their possibilities. 1

T-W-2Mathcad - possibilities of the program and basics of its operation. Solving of basic tasks such as:calculations, matrix operations, plotting, creating of user functions, solving equations, transforming ofsymbolic expressions.

3

T-W-3MATLAB - a program for engineering calculations. General assumptions of the system design. Theorganization of work with the system. The definition of variables in MATLAB. Methods of matrixcreation. Basic matrix operations.

2

T-W-4 Basics of programming in MATLAB (scripts, functions, control commands). 2

T-W-5 Types of variables and data structures in MATLAB and related commands. 2

T-W-6 2D and 3D plots in MATLAB. 2

T-W-7 Fundamentals of data analysis in MATLAB. The basic numerical procedures. Overview of selectedtoolboxes. 2

T-W-8 Evaluation of knowledge. 1


Software for engineering calculations

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Student workload - forms of activity Number of hoursParticipation in labs. 30A-L-1

Participation in lectures and evaluation. 15A-W-1

Self preparing to final evaluation. 15A-W-2

Teaching methods / toolsM-1 Lecture with presentation.

M-2 Lab - solving of selected problems in Mathcad.

M-3 Lab - programming of selected problems in MATLAB.

Evaluation methods (F - progressive, P - final)S-1 Laboratory: evaluation of tasks realized during the classes.F

S-2 Lecture: written test.P



study





education

Teachingmethods


methods

Knowledge

C-1 S-2T-W-1

M-1WM-WI_1-_??_W01The student knows the most important functions andpossibilities of Mathcad.

T-W-2

C-2 S-2T-W-3T-W-4T-W-5 M-1

WM-WI_1-_??_W02The student knows capabilities of MATLAB and its language(syntax, use, available functions, categories of tasks that can besolved with it).

T-W-6T-W-7

Skills

C-1 S-1T-L-1T-L-2T-L-3

M-2WM-WI_1-_??_U01The student has the ability to use Mathcad in engineering andscientific calculations.

T-L-4T-L-5

C-2 S-1T-L-6T-L-7T-L-8T-L-9

M-3WM-WI_1-_??_U02The student has the ability to use MATLAB in engineering andscientific calculations and to program in its language.

T-L-10T-L-11T-L-12




3,0 The student knows the most important functions and possibilities of Mathcad at the basic level.

3,54,04,55,0

WM-WI_1-_??_W02 2,03,0 The student knows capabilities of MATLAB and its language at the basic level.

3,54,04,55,0


3,0 The student can use Mathcad in engineering calculations at the basic level.

3,54,04,55,0

WM-WI_1-_??_U02 2,03,0 The student can use MATLAB and program in its language at the basic level.

3,54,04,55,0

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Required reading1. The Mathworks Inc., MATLAB - the language of Technical Computing, available online, 2015

2. PTC, PTC Mathcad Tutorials, avaiable online, 2016

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Field of study

Mode of study


ECTS


Code

Electives

Language

Elective group


stationary

WI-1-JSW

4,0

credits english

ECTS (forms) 4,0

Level first cycle

Area(s) of study


Module

Course unit Web application development with Angularframework






Małachowski Bartłomiej ([email protected])Leading teacher

Other teachers

PrerequisitesW-1 At least intermediate level in object-oriented programming

W-2 Good knowledge on HTML and CSS languages


C-1 After the course a student will be able to independently develop Angular app with web service data-exchange andauthentication capabilities.

Course content divided into various forms of instruction Number of hoursT-L-1 Setting up environement for writing and running angular apps 2

T-L-2 Wrting simple components 4

T-L-3 Writing simple services 2

T-L-4 Handling simple REST web service in Angular app 4

T-L-5 Handling forms in angular app 4

T-L-6 Development of simple CRUD app 8

T-L-7 Adding authentication to angular app 4

T-L-8 Angular app testing 2

T-W-1 Basic concepts of Angular framework: architecture (modules, components, services), TypeScript vsJavascript, AngularJs vs Angular 2

T-W-2 Principles of writing and running Angular apps: settig up environment, command line tools, appcreation, scalfolding, running an application in development mode, building of production ready app 2

T-W-3 Working with components and databinding 2

T-W-4 Services and dependency injection 2

T-W-5 Routing 1

T-W-6 Handling forms 2

T-W-7 Making HTTP requests 2

T-W-8 Authentication in angular apps 2

Student workload - forms of activity Number of hoursParticipation in classes 30A-L-1

Individual work with given tasks 20A-L-2

Preparations to classes and reading 10A-L-3


Self study 15A-W-2

Exam 1A-W-3

Web application development with Angular framework

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Teaching methods / toolsM-1 Individual work - programming tasks

M-2 Auditorial lectures

Evaluation methods (F - progressive, P - final)S-1 Final examP

S-2 Evalution of developed programming tasks through code review made by the teacherF



study





education

Teachingmethods


methods

Knowledge

Skills



Knowledge

Skills


Required reading1. Angular project team, Angular framework official docs and tutorials, Angular project, web, 2019, 7, https://angular.io/docs

2. Greg Lim, Beginning Angular with Typescript, Greg Lim, 2018, 3

Supplementary reading1. Nathan Murray, Felipe Coury, Ari Lerner, Carlos Taborda, ng-book: The Complete Guide to Angular, Fullstack.io, San Francisco, USA,2018

[ logo uczelni ]

Field of study

Mode of study


ECTS


Code

Electives

Language

Elective group


stationary

WI-1-

4,0

credits russian

ECTS (forms) 4,0

Level first cycle

Area(s) of study


Module

Course unitАЛГОРИТМИЧЕСКИЕ ПРИЁМЫ И ТРЮКИ ВЦИФРОВОЙ ОБРАБОТКЕ СИГНАЛОВ ИИЗОБРАЖЕНИЙ






Cariow Aleksandr ([email protected])Leading teacher

Cariow Aleksandr ([email protected])Other teachers

Prerequisites

W-1 Предмет не требует каких-либо специальных знаний. Все необходимые теоретические сведения и понятия будутпредоставлены и объяснены в процессе проведения занятий.


C-1

Целью освоения настоящей дисциплины является формирование и систематизация знаний в области обработкисигналов и изображений представленных в цифровой форме.

Задачей дисциплины является:- обучение студентов теоретическим знаниям построения систем цифровой обработки сигналов и изображений, атакже алгоритмическим трюкам и приёмам, приводящим к снижению вычислительной сложности разрабатыавемыхалгоритмов и процессорных структур.- привитие студентам практических навыков по методологии инженерных расчетов основных характеристик ипоказателей эффективности разрабатываемых алгоритмов.


T-A-1 Изучение элементов алгебры кронекеровых произведений, как наиболее удобной формыописания, синтеза и реализации алгоритмов ЦОС и ЦОИ.. 2

T-A-2Изучение набора базовых (эталонных) структур матриц, допускающих эффективнуюфакторизацию, приводящую к минимизации арифметической сложности реализациимакроопераций ЦОС и ЦОИ.

2

T-A-3Изучение универсальной методики рационализации вывчислений векторно-матричныхпроизведений. Рассмотрение примера, Решение практических задач на разработку быстрыхалгоритмов вычисления векторно-матричных произведений.

2

T-A-4Определение и выдача индивидуальных заданий на разработку конкретных алгоритмов ЦОС иЦОИ, характеризующихся уменьшенной арифметической сложностью и использующихпринципы распараллеливания вычислений.

2

T-A-5 Обсуждение текущего состояния решения индивидуальныз заданий, связанных спроектированием алгоритмов. Консультации. Подсказки. 2

T-A-6 Обсуждение текущего состояния решения индивидуальныз заданий, связанных спроектированием алгоритмов. Консультации. Подсказки. 2

T-A-7 Зачётное занятие. Оценка и обсуждение правильности решения индивидуальных заданий.Обсуждение достоинств и недостатков предложенных решений. Заключительная дискуссия. 2

T-W-1 Обзор основных методов и задач цифровой обработки сигналов (ЦОС) и изображений (ЦОИ). 2

T-W-2 Изучение известных алгоритмических приёмов и трюков, позволяющих сократить объёмвычислений при решении задач ЦОС и ЦОИ (методы Штрассена, Винограда, трюк Гаусса и т.д.) 2

T-W-3 Представление основных макроопераций цифровой обработки сигналов и изображений спомощью объектов алгебры матриц 2

T-W-4

Рационализация вычисдений векторно-матричных произведений. Демонстрация новых приемови способов сокращения количества арифметических операций при вычислении векторно-матричных произведений. Универсальная (авторская) методика синтеза быстрых алгоритмовалгоритмов вычисления векторно-матричных произведений. Примеры синтеза алгоритмоввекторно-матричных преобразований с уменьшенным числом арифметических операций.

2

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T-W-5Синтез быстрых алгоритмов для решения основных задач ЦОС и ЦОИ (круговая и линейнаясвертка, FDWT / IDWT, DCT, DFT, Хартли, Хаара, Уолша-Адамара, Слэнт-преобразование и другихдискретных преобразований).

2

T-W-6Распараллеливание вычислений, как способ сокращения времени реализации вычислительныхпроцессов. параллельные алгоритмы векторно-матричных произведений. параллельныеалгоритмы реализации базовых макроопераций ЦОС и ЦОИ.

2

T-W-7Обсужление возможностей и особенностей построения эффективных структурспециализированных процессорных узлов и модулей, использующих преимущества,получаемых от вводимых рационализаций. Заключение.

2

T-W-8 Зачётное занятие, выставление оценок за теоретическую часть предмета. 1

Student workload - forms of activity Number of hoursУчастие в занятиях. 15A-A-1

Выполнение домашних заданий 30A-A-2

Консультации по вопросам решения домашних заданий. 15A-A-3

Участие в занятиях. 15A-W-1

Подготовка к лекциям 30A-W-2

Консультации 15A-W-3

Teaching methods / toolsM-1 Лекции с использованием мультимедийных презентаций и практические занятия.

Evaluation methods (F - progressive, P - final)S-1 Оценки за решение домашних заданий. В конце - экзамен в форме опроса.F



study





education

Teachingmethods


methods

Knowledge

Skills


C-1 S-1M-1

WM-WI_1-_??_K01В результате освоения дисциплины обучающиеся должны

знать:

- основные алгоритмические трюки и способырационализации алгоритмов ЦОС, предназначенных дляреализации в FPGA;- уметь:

- синтезировать высокоэффективные алгоритмы ЦОС,подходящие для реализации на FPGA;

- описывать вычислительные процедуры в матричнойформе, верифицировать их с помощью моделирования;

-владеть:- навыком освоения большого объема информации;- навыками постановки научно-исследовательских задач инавыками самостоятельной работы.


Knowledge

Skills


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Other social / personal competencesWM-WI_1-_??_K01

2,0Выставляется студенту, который не знает большей части основного содержания учебной программы дисциплины,допускает грубые ошибки в формулировках основных принципов и не умеет использовать полученные знания прирешении типовых задач.

3,0 Выставляется студенту, если он твердо знает материал, грамотно и по существу излагает его, умеет применятьполученные знания на практике, но допускает в ответе или в решении задач некоторые неточности.

3,5 Выставляется студенту, если он твердо знает материал, грамотно и по существу излагает его, умеет применятьполученные знания на практике, но недостаточно грамотно обосновывает полученные результаты.

4,0Выставляется студенту, показавшему систематизированные, глубокие знания учебной программы дисциплины иумение уверенно применять их на практике при решении конкретных задач, правильное обоснование принятыхрешений, с некоторыми недочетами.

4,5Выставляется студенту, показавшему всесторонние, систематизированные, глубокие знания учебной программыдисциплины и умение уверенно применять их на практике при решении конкретных задач, свободное и правильноеобоснование принятых решений.

5,0Выставляется студенту, показавшему всесторонние, систематизированные, глубокие знания учебной программыдисциплины, проявляющему интерес к данной предметной области, продемонстрировавшему умение уверенно итворчески применять их на практике при решении конкретных задач, свободное и правильное обоснованиепринятых решений.

Required reading1. Блейхут Р., Быстрые алгоритмы цифровой обработки сигналов, Пер. с англ. - М.: Мир, Москва, 1989, - 448с.2. Нуссбаумер Г., Быстрое преобразование Фурье и алгоритмы вычисления сверток, Пер. с англ. - М.: Радио и связь, Москва,1985, - 248с.3. Хуанг Т. С., Эклунд Дж. О., Нуссбаумер Г., Быстрые алгоритмы в цифровой обработке изображений, Пер. с англ. М.: Радио исвязь, Москва, 1984, - 224 с.4. Макклеллан Дж.Г., Рейдер Ч.М., Применение теории чисел в цифровой обработке сигналов, Изд-во: М.: Радио и связь, Изд-во:М.: Радио и связь, 1983 г.;осква, 1983, - 264 с.

Supplementary reading1. Власенко В.А., Лаппа Ю.М., Ярославский Л.П., Методы синтеза быстрых алгоритмов свертки и спектрального анализасигналов, М.: Наука, М.: Наука, 1990. — 180 с.осква, 1990, — 180 с.

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Field of study

Mode of study


ECTS


Code

Electives

Language

Elective group


stationary

WI-1-AOC

5,0

credits russian

ECTS (forms) 5,0

Level first cycle

Area(s) of study


Module

Course unit Алгоритмические основы цифровой обработкисигналов и изображений






Cariow Aleksandr ([email protected])Leading teacher

Cariow Aleksandr ([email protected]), Cariowa Galina ([email protected])Other teachers

Prerequisites

W-1Требования к предварительной подготовке обучающегося:

Знание основ элементарной математики, матричной алгебры, цифровой техники.


C-1

Целью освоения настоящей дисциплины является формирование и систематизация знаний в области обработкисигналов и изображений представленных в цифровой форме.Основной задачей дисциплины является обучение студентов теоретическим знаниям и алгоритмам построениясистем цифровой обработки сигналов и изображений, а также привитие им практических навыков по методологииинженерных расчетов основных характеристик и показателей эффективности изучаемых алгоритмов и систем.


T-A-1

Элементы матричной алгебры. Представление одномерного сигнала в виде вектора,двумерного (изображения) - в виде матрицы. Специальные типы матриц. Единичная и нулеваяматрицы. Матрицы сдвига, перестановки, растяжения, дублирования. Изучение операцийконкатенации, тензорного (кронекеровского) произведения, прямой суммы. Графическоепредставление алгоритмов ЦОС в виде сигнальных графов.

2

T-A-2Изучение и ислледование особкнностей векторно-матричных процедур БПФ. Решение примеровна построение алгоритмов БПФ (по основанию 2 и 4) для конкретных значений исходныхпоследовательностей данных.

2

T-A-3Изучение особенностей построения быстрых алгоритмов дискретных ортогональныхпреобразований (ДОП) для различных длин исходных последовательностей данных. Решениезадач на построение быстрых алгоритмов ДОП Уолша, Хаара, Хартли и т.д.

2

T-A-4Решение задач на построение быстрых алгооитмов одномерной и двумерной свёртки.Разработка алгоритмов быстрой свёртки (круговой и линейной) во временной и частотнойобластях.

2

T-A-5 Решение задач на применение методов "overlap-save" и "overlap-add". 2

T-A-6 Решение задач на построение алгоритмов прямого и обратного дискретного вейвлет-преобразования в базисе фильтров Добеши. 2

T-A-7 Зачётное занятие. Подведение итогов изучения предмета и выставление оценок. 2

T-W-1Введение. Аналитический обзор и обсуждение основных задач, методов и приложенийцифровой обработки сигналов (ЦОС). История ЦОС. Преимущества ЦОС. Достоинства инедостатки ЦОС.

2

T-W-2Элементы матричной алгебры. Представление основных операций цифровой обработкисигналов и изображений с помощью объектов алгебры матриц (в том числе в виде матрично-матричных и векторно-матричных произведений).

2

T-W-3Спектр цифрового сигнала. Дискретное преобразование Фурье (ДПФ). Свойства ДПФ. Быстроепреобразование Фурье (БПФ), алгоритмы с прореживанием по времени и частоте. Операция"бабочка". Двоично-инверсная адресация. Алгоритм Винограда. ДПФ действительныхпоследовательностей.

2

T-W-4Обобщение ДПФ. Дискретные ортогональные преобразования в базах Уолша, Хаара, Виленкина,Хартли. Дискретное косинус-преобразование. Быстрые алгоритмы дискретных ортогональныхпреобразований в перечисленных базисах.

2

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T-W-5

Цифровые свёртка и корреляция. Круговая и линейная свёртка. Быстрые алгоритмы вычислениякруговой свёртки. Цифровая фильтрация. ФИльтры КИХ и БИХ. Реализация операциифильтрации с помощью дискретных ортогональных преобразований. Вычисление линейнойсвёртки с помощью круговой. Фильтрация длинных последовательносьтей: методы "overlap-save" и "overlap-add".

2

T-W-6Вейвлет-технологии. История. Определение вейвлета. Многоуровневая декомпозиция иреконструкция. Алгоритм Малла - дискрентое вейвлет-преобразование. Фильтры Добеши.Вычислительные процедуры дискретного вейвлет-преобразования. Вейвлетоподобныепреобразования.

2

T-W-7Элементная база процессоров цифровой обработки сигналов и изображений. Тенденцииразвития специализированных микросистем ЦОС. Распараллеливание вывчислений:конвейерная и векторная обработка данных. Обзор и обсуждение достоинств и недостатковсовременных параллельных СБИС-структур, ориентированных на реализацию задач ЦОС.

2

T-W-8 Диалектические аспекты ускорения вычислений. Высокопроизводительные вычисления:единство и борьба противоположностей. 1

Student workload - forms of activity Number of hoursУчастие в занятиях 15A-A-1

Самостоятельная работа. Выполнение домащних заданий. 40A-A-2Консультации, касающиеся домашних заданий Проверка правильности решений. Устранениеошибок. 20A-A-3

Участие в занятиях. 15A-W-1Самостоятельная работа. Изучение пройденного в аудитории материала. Подготовка клекционным занятиям. 40A-W-2

Консультации. 20A-W-3

Teaching methods / toolsM-1 Лекции и практические занятия с использованием мультимедийных презентаций.


S-1Отличная оценка выставляется студенту, показавшему всесторонние, систематизированные, глубокиезнания учебной программы дисциплины, проявляющему интерес к данной предметной области,продемонстрировавшему умение уверенно и творчески применять их на практике при решении конкретныхзадач, свободное и правильное обоснование принятых решений.

F

S-2Четвёрка с плюсом выставляется студенту, показавшему всесторонние, систематизированные, глубокиезнания учебной программы дисциплины и умение уверенно применять их на практике при решенииконкретных задач, свободное и правильное обоснование принятых решений.

F

S-3Четвёрка выставляется студенту, показавшему систематизированные, глубокие знания учебной программыдисциплины и умение уверенно применять их на практике при решении конкретных задач, правильноеобоснование принятых решений, с некоторыми недочетами.

F

S-4Оценка 3,5 выставляется студенту, если он твердо знает материал, грамотно и по существу излагает его,умеет применять полученные знания на практике, но недостаточно грамотно обосновывает полученныерезультаты.

F

S-5Оценка 3 выставляется студенту, если он твердо знает материал, грамотно и по существу излагает его,умеет применять полученные знания на практике, но допускает в ответе или в решении задач некоторыенеточности.

F

S-6

Оценка 2 в ыставляется студенту, показавшему фрагментарный, разрозненный характер знаний,допускающему ошибки в формулировках базовых понятий, нарушения логической последовательности визложении программного материала, слабо владеет основными разделами учебной программы,необходимыми для дальнейшего обучения и с трудом применяет полученные знания даже в стандартнойситуации.

F



study





education

Teachingmethods


methods

Knowledge

C-1

S-1S-2S-3S-5S-6

M-1

WM-WI_1-_??_W01Знать:- преимущества цифровой обработки сигналов и иё роль впроектировании приборов, устройств и узловтелекоммуникационных информационных систем;- математический аппарат для описания цифровых сигналови изображений;- основные методы и алгоритмы цифровой обработкисигналов и изображений;- области применения цифровой обработки сигналов;- современную элементную базу для реализации системцифровой обработки сигналов;

Skills

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WM-WI_1-_??_U01Уметь:- математически описывать цифровые сигналы иизображения;- проектировать (проводить синтез и рассчитыватьпараметры) базовых алгоритмов цифровой обработкисигналов и изображений;- применять полученные знания и методы обработкисигналов для решения практических задач ЦОС и ЦОИ,- самостоятельно приобретать новые знания в областицифровой обработки сигналов и изображений.

Other social / personal competencesWM-WI_1-_??_K01Владеть:- математическими и алгоритмическими методамипроектирования и оценки систем цифровой обработкисигналов;- ориентироваться в современной литературе по цифровойобработке сигналов.


KnowledgeWM-WI_1-_??_W01

2,0Выставляется студенту, который не знает большей части основного содержания учебной программы дисциплины,допускает грубые ошибки в формулировках основных принципов и не умеет использовать полученные знания прирешении типовых задач.

3,0 Выставляется студенту, если он твердо знает материал, грамотно и по существу излагает его, умеет применятьполученные знания на практике, но допускает в ответе или в решении задач некоторые неточности.

3,5 Выставляется студенту, если он твердо знает материал, грамотно и по существу излагает его, умеет применятьполученные знания на практике, но недостаточно грамотно обосновывает полученные результаты.

4,0Выставляется студенту, показавшему систематизированные, глубокие знания учебной программы дисциплины иумение уверенно применять их на практике при решении конкретных задач, правильное обоснование принятыхрешений, с некоторыми недочетами.

4,5Выставляется студенту, показавшему всесторонние, систематизированные, глубокие знания учебной программыдисциплины и умение уверенно применять их на практике при решении конкретных задач, свободное и правильноеобоснование принятых решений.

5,0Выставляется студенту, показавшему всесторонние, систематизированные, глубокие знания учебной программыдисциплины, проявляющему интерес к данной предметной области, продемонстрировавшему умение уверенно итворчески применять их на практике при решении конкретных задач, свободное и правильное обоснованиепринятых решений.


3,03,54,04,55,0


3,03,54,04,55,0

Required reading1. Рабинер Л. Гоулд Б., Теория и применение цифровой обработки сигналов., Пер. с англ. Зайцева А.Л. Назаренко Э.Г. - М: Мир,Москва, 1978, - 835с.2. Дагман, Э.Е.; Кухарев, Г.А., Быстрые дискретные ортогональные преобразования, Издательство: Наука, Новосибирск, 1983, -232 с.3. Юкио Сато, Обработка сигналов: первое знакомство, М: Додэка-XXI, 2010, – 176 с.

4. Прэтт У., Цифровая обработка изображений, Пер. с англ.—М.: Мир, Пер. с англ.—М.: Миросква, 1982, два тома, — 312 с.

5. Блейхут Р, Быстрые алгоритмы цифровой обработки сигналов, Мир, Москва, 1989, - 448с.6. Нуссбаумер Г., Быстрое преобразование Фурье и алгоритмы вычисления сверток, Пер. с англ. - М.: Радио и связь, Москва,1985, - 248с.7. Ахмед Н., Рао К.Р., Ортогональные преобразования при обработке цифровых сигналов, Пер. с англ. — М.: "Связь", Москва,1980, — 248 с.8. Хуанг Т. С., Эклунд Дж. О., Нуссбаумер Г., Быстрые алгоритмы в цифровой обработке изображений, Перю с англ.б М.: Радио исвязь,, Москва, 1984, — 224 с.

Supplementary reading1. Айфичер Э., Джервис Б., Цифровая обработка сигналов. Практический подход, М.: Изд. дом «Вильямс», Москва, 2004, – 992 с.2. Стивен, С. Цифровая обработка сигналов. Практическое руководство, Цифровая обработка сигналов. Практическоеруководство для инженеров и научных работников, М.: Додэка – XXI, Москва, 2008, -720 с.

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Supplementary reading3. Лэй Э., [9].Цифровая обработка сигналов для инженеров и технических специалистов, М.: Группа ИДТ, [9]. Лэй, Э.Цифровая обработка сигналов для инженеров и технических специалистов: практическое руководство / Э. Лэй. – М.: ГруппаИДТ, 2007. – 336 с., 2007, – 336 с.

SECOND DEGREE (MASTER)

[ logo uczelni ]

Field of study

Mode of study


ECTS


Code

Electives

Language

Elective group


stationary

WI-2-A3P

6,0

credits english

ECTS (forms) 6,0

Level second cycle

Area(s) of study


Module

Course unit Arduino 3D Printer







Jaszczak Sławomir ([email protected])Other teachers


Module/course unit objectivesC-1 Acquire the basic knowledge on Arduino platform

C-2 The practical skills of the hardware and software synthesis by using Arduino

Course content divided into various forms of instruction Number of hoursT-L-1 Arduino - writing simple program 10

T-L-2 Arduino project - Hardware and Software synthesis 19

T-L-3 Exam 1

T-W-1 History of Arduino 2

T-W-2 Arduino - official boards 6

T-W-3 Arduino - shields 6

T-W-4 Arduino - programming 15

T-W-5 Exam 1






M-2 Discussion



S-2 Written examP



study





education

Teachingmethods


methods

Knowledge

Arduino 3D Printer

[ logo uczelni ]

C-1 S-2T-W-1T-W-2 M-1

M-2WM-WI_2-_null_W01After the lectures the student will be able to describe Arduinoboards and shields

T-W-3T-W-4

Skills

C-2 S-1T-L-1 M-2M-3

WM-WI_2-_null_U01The student will be able to write program for Arduino platform

T-L-2




3,0 The student is able to define and describe Arduino boards and shields

3,54,04,55,0


3,0 The student is able to make hardware and software synthesis by using Arduino platform

3,54,04,55,0



[ logo uczelni ]

Field of study

Mode of study


ECTS


Code

Electives

Language

Elective group


stationary

WI-2-ARD

6,0

credits english

ECTS (forms) 6,0

Level second cycle

Area(s) of study


Module

Course unit Arduino – an introduction to the Internet of Things







Other teachers

PrerequisitesW-1 Basics of: C programming, electronics and computer systems architecture.


C-1To gain:1. theoretical and practical skills in Arduino programming,2. ability of advanced hardware projects preparation.


T-L-1

1. Introduction to Arduino, its hardware and software design, IDE.2. The art of Arduino programming – sketch and its structure: setup(), loop(), comments; data types;variables; arithmetic, logical, conditional, relational, increment operators; constants; functions; flowcontrol: if, if...else, for, while, do...while; arrays; strings; digital I/O; analog I/O; time; math; random;serial communication; libraries; PWM; interrupts; I2C; SPI; SD card; wired and wireless networking.3. Detailed overview of all sensors that will be used during laboratory.4. Examples built-in the IDE. Hello world! sketch.5. Using of breadboard, resistors and LEDs, buttons, switches, digital inputs, analog inputs, digitaloutputs, PWM.6. Light: LED, fading LED, 2-color LED, RGB LED, LED bar graph, 7-digits LED display, dot-matrix LEDdisplay, LCD display.7. Sensors: humidity, temperature, pressure, raindrops, PIR, ultrasonic, sound, knock, vibration, photoresistor, tilt, infrared, Hall magnetic, rotary encoder, flame, joystick, metal touch, mercury switch,detection of gases, 3D accelerometer, obstacle avoidance IR, optical broken light, laser.8. Outputs: motor control: DC motor, servo motor, stepper motor; relay module9. Sound: tone library, microphone, buzzer, speaker.10. Analog and digital inputs: reading analog voltage, external keyboard and mouse.11. RFID module, SD storage, GPS receiver.12. Ethernet shield, wireless communication.

30

T-P-1Implementation of selected problem:1. Hardware design proposal.2. Software implementation of the problem's solution.3. Preparation of the project's documentation.

30





Attendance in the classes 30A-P-1

Completing of the project 60A-P-2

Teaching methods / toolsM-1 Laboratory work and project

Arduino ? an introduction to the Internet of Things

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S-1 Laboratory – evaluation of the reports submitted after each classProject – evaluation of the final project, along with its documentationP



study





education

Teachingmethods


methods

Knowledge

SkillsWM-WI_2-_null_U01Student will gain theoretical and practical skills in Arduinoprogramming, along with ability of advanced hardware projectspreparation



Knowledge


3,03,54,04,55,0


Required reading1. Michael Margolis, Arduino cookbook, O’Reilly, 2013

2. John Boxall, Arduino workshop: a hands on introduction with 65 projects, No Starch Press, 2013

3. Arduino Home https://www.arduino.cc/

Supplementary reading1. Adeel Javed, Building Arduino projects for the Internet of Things: experiments with real-world applications, Apress, 2016

[ logo uczelni ]

Field of study

Mode of study


ECTS


Code

Electives

Language

Elective group


stationary

WI-2-BIA

6,0

credits english

ECTS (forms) 6,0

Level second cycle

Area(s) of study


Module

Course unit Biologically inspired algorithms







Other teachers

PrerequisitesW-1 Basic programming skills

Module/course unit objectivesC-1 To introduce and discuss algorithm that were inspired by biological phenomenon (part of Artificial Intelligence domain).

C-2 Application of different algorithms in various real and test problems

Course content divided into various forms of instruction Number of hoursT-L-1 Optimization - simple heuristics 2

T-L-2 Genetic algorithm implementation 4

T-L-3 Evolution strategies implementation 4

T-L-4 Particle Swarm Optimization algorithm implementation 2

T-L-5 Differential evolution implementation 2

T-L-6 Ant colony optimization for discrete problems - implementation 2

T-L-7 Immune systems - Clonalg, anomaly detection 4

T-L-8 Neural networks - supervised learning - implementation 3

T-L-9 Neural network - usupervised 3

T-L-10 Hybrid solutions - implementation 4

T-W-1 Computation intelligence - introduction 2

T-W-2 Evolutionary algorithm 4

T-W-3 Optimization task - chalanges 2

T-W-4 Evolution strategies 2

T-W-5 Differential evolution 2

T-W-6 Particle Swarm Optimization as a robust optimization method in continues domain 2

T-W-7 Ant colony optimization for discrete problems. 2

T-W-8 Artificial Immune Systems as an optimization tool 4

T-W-9 Neural networks - supervised 5

T-W-10 Neural networks - unsupervised 3

T-W-11 Hybrid methaheuristics 2


Homeworks - algorithms implementation, analysis, raports 50A-L-2

Biologically inspired algorithms

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Student workload - forms of activity Number of hoursSelfstuying - reading 10A-L-3

Lectures participation 30A-W-1

Literature/articles reading 30A-W-2

Preparing to the test 30A-W-3

Teaching methods / toolsM-1 Lecture with presentation and conversation

M-2 Software development.

Evaluation methods (F - progressive, P - final)S-1 Test checking the knowlage on biologicaily inspired algorrithms.P

S-2 Examination of programming tasksF



study





education

Teachingmethods


methods

Knowledge

C-1 S-1


M-1WM-WI_2-_null_W01Student will know how to apply different algorithms and will beaware of the power, and the limitations, of discussed during thecourse methods.

T-W-7T-W-8T-W-9T-W-10T-W-11

Skills

C-2 S-2


M-2WM-WI_2-_null_U01Practical skills of implementing, analysing and testingalgorithms described during the course.

T-L-6T-L-7T-L-8T-L-9T-L-10




3,0 Students can describe how the algorithms discussed during the cours works.

3,54,04,55,0


3,0 Implementation of algorithms basic variants

3,54,04,55,0


Required reading1. Thomas Weise, Global Optimization Algorithms - Theory and Application, online book, 2011

[ logo uczelni ]

Field of study

Mode of study


ECTS


Code

Electives

Language

Elective group


stationary

WI-2-BCI

4,0

credits english

ECTS (forms) 4,0

Level second cycle

Area(s) of study


Module

















T-L-5 Exam. 2






T-W-6 Exam. 2






M-2 Discussion.





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study





education

Teachingmethods


methods

Knowledge

C-1 S-2

T-L-5T-W-1T-W-2 M-1

M-2


T-W-3T-W-4T-W-5

Skills

C-2 S-1T-L-1T-L-2T-L-3


T-L-4T-L-5





3,54,04,55,0



3,54,04,55,0



[ logo uczelni ]

Field of study

Mode of study


ECTS


Code

Electives

Language

Elective group


stationary

WI-2-CPL

4,0

credits english

ECTS (forms) 4,0

Level second cycle

Area(s) of study


Module

Course unit C# Programming Language







Other teachers


Module/course unit objectivesC-1 Familiar with the sytnax, structures and principles used in the c# language

C-2 The ability to develop an object-oriented program in c# language.

Course content divided into various forms of instruction Number of hoursT-L-1 Introduction to visual Studio IDE and C# 2

T-L-2 Data types, operators 2

T-L-3 Controlling Programmatic Flow 2

T-L-4 Exceptions 2

T-L-5 Constructing Complex Types: classes and structs 4

T-L-6 Inheritance, Abstraction, Object Interfaces 4

T-L-7 Generic Types 2

T-L-8 Generic Collections 2


T-L-10 Threading, parallelism and asynchronous operations 4

T-L-11 Windows Forms Applications 4

T-W-1 Introduction to: Object Oriented Programming, Managed Languages and C# 2

T-W-2 Controlling Programmatic Flow, Manipulating Types 2

T-W-3 Constructing Complex Types, Object Interfaces and Inheritance 4

T-W-4 Generic Types and Collections 2

T-W-5 Input-output operations and multi threading 2

T-W-6 Windows Forms Applications 2

T-W-7 Exam 1






C# Programming Language

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M-2 Discussion



S-2 written examP



study





education

Teachingmethods


methods

Knowledge

C-1 S-2T-W-1T-W-2T-W-3

M-1M-2

WM-WI_2-_null_W01After the course the student will know the c# syntax and will beable to define object-oriented programming principles in thecontext of c#

T-W-4T-W-5T-W-6

C-2 S-2T-W-1T-W-2T-W-3

M-1M-2

WM-WI_2-_null_W02After the course the student will be able to explain what ishappening in a c# code.

T-W-4T-W-5T-W-6

Skills

C-1C-2 S-1


M-2M-3

WM-WI_2-_null_U01The student will be able to write program in a c# language.

T-L-7T-L-8T-L-9T-L-10T-L-11




3,0 The student knows c# syntax.

3,54,04,55,0

WM-WI_2-_null_W02 2,03,0 The student is able to explain code of a simple program written in c#.

3,54,04,55,0


3,0 The student is able to write a simple program in a c# language.

3,54,04,55,0


Required reading1. John Sharp, Microsoft Visual C# 2012 Step by Step, 20132. Karli Watson, Jacob Vibe Hammer, Jon Reid, Morgan Skinner, Daniel Kemper, Christian Nagel, Beginning Visual C# 2012 Programming,2012

Supplementary reading1. http://en.wikibooks.org/wiki/C_Sharp_Programming

[ logo uczelni ]

Field of study

Mode of study


ECTS


Code

Electives

Language

Elective group


stationary

WI-2-C++

4,0

credits english

ECTS (forms) 4,0

Level second cycle

Area(s) of study


Module

Course unit C++ programming language







Other teachers


Module/course unit objectivesC-1 Familiar with the syntax, basic programming constructs and principles used in C++ language

C-2 The ability to write small-scale C++ programs using the acquired skills

Course content divided into various forms of instruction Number of hoursT-L-1 Introduction to C++ and IDE 2

T-L-2 Variables, datatypes and operators 3

T-L-3 Input/output operations 3

T-L-4 Conditionals 4

T-L-5 Loops 5

T-L-6 Arrays 4

T-L-7 Structures 3

T-L-8 Functions 4

T-L-9 Input/output with files 2

T-W-1 Introduction to programming and C++ 2

T-W-2 Structure of a program and basic concepts 2

T-W-3 Variables and fundamental data types 3

T-W-4 Input/output operations 3

T-W-5 Constants and operators 3

T-W-6 Conditionals and loops 6

T-W-7 Arrays and multi-dimensional arrays 4

T-W-8 Structures 2

T-W-9 Functions 4

T-W-10 Exam 1





C++ programming language

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M-2 Discussion



S-2 Continuous assessmentF



study





education

Teachingmethods


methods

KnowledgeWM-WI_2-_??_W01After the course the student should be able to Understand anduse the basic programming constructs of C++ and write small-scale C++ programs using the above skillsWM-WI_2-_??_W02After the course the student should be able to explain what ishappening in a C++ code

Skills




3,03,54,04,55,0

WM-WI_2-_??_W02 2,03,03,54,04,55,0

Skills


Required reading1. Bjarne Stroustrup, The C++ Programming Language (Fourth Edition), Addison-Wesley, 2012

2. Daoqi Yang, C++ and Object-Oriented Numeric Computing for Scientists and Engineers, Springer, 2001

3. http://www.cplusplus.com/doc/tutorial/

Supplementary reading1. https://en.wikibooks.org/wiki/C%2B%2B_Programming

[ logo uczelni ]

Field of study

Mode of study


ECTS


Code

Electives

Language

Elective group


stationary

WI-2-CPR

4,0

credits english

ECTS (forms) 4,0

Level second cycle

Area(s) of study


Module

Course unit Cloud programming







Other teachers

Prerequisites

W-1 Basic knowledge and skills in object-oriented programming (preferably in Java, C# and/or Python), databases, webapplications development.

Module/course unit objectivesC-1 Familiarizing with selected cloud platforms.

C-2 Possess knowledge and obtain practical skills in developing cloud-based applications.

C-3 Familiarizing with technologies, techniques and tools for cloud development.


Course content divided into various forms of instruction Number of hoursT-L-1 Introduction to cloud computing. Setting up development environment. Overview of the lab classes. 2

T-L-2 Cloud-based data storage. 2

T-L-3 Security in cloud-based appliations. 2

T-L-4 Analytical and predictive services 2

T-L-5 Multimedia services 2

T-L-6 Other and external services - integration with other providers. 2

T-L-7 Services for mobile devices. 2

T-L-8 Internet of Things. Management tools. 2

T-L-9 DevOps. Deployment and testing cloud-based applications. 2

T-L-10 Developing a student project 10

T-L-11 Project presentations and grading 2

T-W-1 Introduction to cloud computing – features, capabilities and limitations. 1

T-W-2 Cloud computing platforms. Overview of the main services. 1

T-W-3 Cloud-based data storage. 2

T-W-4 Security in cloud-based applications. 2

T-W-5 Analytical and predictive services 2

T-W-6 Multimedia services 2

T-W-7 Other and external services 2

T-W-8 Services for mobile devices. 1

T-W-9 Internet of Things 1



Cloud programming

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preparing for credit 13A-W-3



M-2 Lab exercises

M-3 Project






study





education

Teachingmethods


methods

Knowledge

C-1C-2C-3

S-3

T-L-2T-L-4T-L-5T-L-7T-W-1

M-1WM-WI_2-_null_W01Explains core concepts of cloud computing and cloudprogramming.

T-W-2T-W-4T-W-7T-W-9T-W-10

Skills

C-1C-2C-3C-4

S-1S-2


M-1M-2M-3

WM-WI_2-_null_U01Can develop, deploy and manage cloud-based application.

T-L-7T-L-8T-L-9T-L-10T-L-11


C-1C-2C-3C-4

S-1S-2S-3


M-2M-3

WM-WI_2-_null_K01Student has increased awareness and motivation of self-learningof rapidly developing cloud technologies.

T-L-6T-L-7T-L-8T-L-9T-L-10



3,0 Student can explain and distinguish majority of core concepts of cloud computing and cloud programming on a singleplatform.

3,54,04,55,0


3,0 Student can develop, deploy and manage a simple cloud-based application on a specific single platform.

3,54,04,55,0

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3,0 Student can gain technical knowledge on cloud technologies with self learning.

3,54,04,55,0

Required reading1. Erl T., Puttini R., Mahmood Z., Cloud Computing: Concepts, Technology & Architecture, Prentice Hall, 2013

2. IBM Cloud Docs, https://bluemix.net/docs/

3. AWS Documentation, https://aws.amazon.com/documentation

4. Google Cloud Documentation, https://cloud.google.com/docs/

Supplementary reading1. Redkar T., Windows Azure Web Sites: Building Web Apps at a Rapid Pace, CreateSpace Independent Publishing Platform, 2013

2. Rhoton J., Cloud Computing Explained: Implementation Handbook for Enterprises, Recursive Press, 2010, 2

3. Sanderson D., Programming Google App Engine, O'Reilly Media, 2012, 2

[ logo uczelni ]

Field of study

Mode of study


ECTS


Code

Electives

Language

Elective group


stationary

WI-2-CTN

4,0

credits english

ECTS (forms) 4,0

Level second cycle

Area(s) of study


Module

Course unit Computer and telecommunication networks







Other teachers

PrerequisitesW-1 Basics of programming; Architecture of computer systems; Operating systems fundamentals.

Module/course unit objectivesC-1 Knowledge of reference models, network standards, protocols of data link layer, network, transport and application layers.

C-2 Knowledge of current wired and wireless network solutions.

C-3 Ability of network’s performance evaluation.

C-4 Ability of simple home/office network building.

C-5 Basic algorithms of data link, network and application layer implementation ability.

Course content divided into various forms of instruction Number of hoursT-L-1 Implementation of the program implementing the CRC algorithm. 8

T-L-2 Implementation of the program implementing the routing algorithm selected. 8

T-L-3 Implementation of the program implementing selected network application (eg. chat, file transfer, etc.) 8

T-L-4 Introduction to simulation of computer networks. Building of a simulation model for a simple network. 6

T-W-1 Introduction to computer networks. 2

T-W-2 Physical layer, transmission media, multiplexing techniques, circuit and packet switching. 4

T-W-3 Data link layer, error detection, flow control, ALOHA and CSMA protocols, protocols without collisions,Ethernet, wireless local area networks, interconnecting. 6

T-W-4 Network layer, routing algorithms and protocols, quality of service, Internet Protocol. 6

T-W-5 Transport layer, protocols, addressing, flow control, UDP, TCP and RTP protocols, Nagle’s and Clarke’salgorithms. 6

T-W-6 Application layer, DNS, e-mail, WWW, multimedia applications of the networks. 6







Exam 3A-W-3

Consultations to the lecture 2A-W-4


Computer and telecommunication networks

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Teaching methods / toolsM-2 Laboratory work

Evaluation methods (F - progressive, P - final)S-1 Lecture - written examP

S-2 Laboratory work - written reportsF

S-3 Laboratory work - evaluation of submitted programs and projectP



study





education

Teachingmethods


methods

Knowledge

C-1C-2 S-1

T-W-1T-W-2T-W-3

M-1WM_2-_null_W01Student will gain detailed knowledge of network technologies

T-W-4T-W-5T-W-6

Skills

C-3 S-2S-3

T-L-4M-2

WM_2-_null_U01Student is capable of running simulation package specialized incomputer networks

C-4C-5

S-2S-3

T-L-1T-L-2 M-2

WM_2-_null_U02Student is able to prepare programs implementing selectednetworking aspects

T-L-3




3,0 Student knows the network layers, can name basic communication protocols, is also familiar with the fundamentals of IPaddressing, network topologies and network technologies that are currently used.

3,54,04,55,0


3,0 Basic ability of using simulation package (Riverbed Modeler) - loading of prepared design, simulation, gathering of theresults.

3,54,04,55,0

WM_2-_null_U02 2,03,0 Basic skills in the implementation of selected networking aspects.

3,54,04,55,0


Required reading1. A. S. Tanenbaum, Sieci komputerowe, Helion, Gliwice, 2004

2. M. Hassan, R. Jain, Wysoko wydajne sieci TCP/IP, Helion, Gliwice, 2004

[ logo uczelni ]

Field of study

Mode of study


ECTS


Code

Electives

Language

Elective group


stationary

WI-2-CSI

6,0

credits english

ECTS (forms) 6,0

Level second cycle

Area(s) of study


Module

Course unit Computer simulation








PrerequisitesW-1 Basic computer techniques, particularly basic file management under Windows, Excel and word processing.

W-2 Basic statistics concepts, probability and stochastic processes, particularly the exponential, normal and uniformdistributions.


C-1 Be able to simulation modeling using a computer; Understand the assumptions, strengths and weaknesses of simulationmodels; Analyze and translate problems into a form suitable for applying simulation strategies; Validate a simulation model.

Course content divided into various forms of instruction Number of hoursT-L-1 Modeling and estimating input processes 4

T-L-2 Modeling basics operations and inputs 4

T-L-3 Statystical analysis of output 4

T-L-4 Transport modeling 4

T-L-5 Symulation analysis project 14

T-W-1 Introduction to modelling 2

T-W-2 Fundamental Simulation Concepts 2

T-W-3 Introduction and overview of simulation analysis 2

T-W-4 Modeling and estimating input processes 2

T-W-5 Statistical analysis of simulation output 2

T-W-6 Comparison, ranking, and selection of simulation models 2

T-W-7 Design of experiment 3


Project 60A-L-2


Homeworks and self study 45A-W-2

Teaching methods / toolsM-1 Lectures with case studies

M-2 Projects

Evaluation methods (F - progressive, P - final)S-1 Project report & presentationP

S-2 Case studies and small projectsF

Computer simulation

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study





education

Teachingmethods


methods

Knowledge

C-1 S-1T-W-1T-W-2

WM-WI_2-_null_W01Understand the assumptions, strengths and weaknesses ofsimulation models;

T-W-3

Skills

C-1 S-1T-L-2T-L-3T-L-4T-L-5

WM-WI_2-_null_U01Analyze and translate problems into a form suitable for applyingsimulation strategies





3,0 Student understands basic terms and notions.

3,54,04,55,0

SkillsWM-WI_2-_null_U01 2,0 Student is not able to model and analyse a simple system.

3,0 Student is able to model and analyse a simple system.

3,5 Student is able to model and analyse simple system with input data analysis.

4,0 Student is able to model and analyse simple system with input and output data analysis.

4,5 Student is able to model and analyse complex system with input and output data analysis.

5,0 Student is able to model and analyse complex system with input, output data analysis and prepare a proper designg ofexperiment.


Required reading1. W. David Kelton, Randall P. Sadowski, and David T. Sturrock, Simulation with Arena, McGraw Hill, New York, 2004, 3

2. J. Banks, J. S. Carson B. L. Nelson, and D. M. Nicol, Discrete-Event System Simulation, Prentice Hall, New York, 2005

Supplementary reading1. Law, A. M. & Kelton, W. D., Simulation Modelling & Analysis, McGraw Hill, Boston, 2000

[ logo uczelni ]

Field of study

Mode of study


ECTS


Code

Electives

Language

Elective group


stationary

WI-2-CVI

4,0

credits english

ECTS (forms) 4,0

Level second cycle

Area(s) of study


Module

Course unit Computer Vision and Fast Object Detection







Other teachers

PrerequisitesW-1 Algorithmics. Mathematics. Good skills in programming.

Module/course unit objectivesC-1 To familiarize students with techniques and algorithms related to detection of objects in digital images.


T-L-1 Student's own implementation of face detector (or human detector) - a project per whole semester(programming languange of student's choice) 15

T-W-1 Computational complexity of detection procedures based on a sliding window. Extraction of imagefeatures using integral images - constant-time cost per feature. Haar wavelets and Haar-like features. 6

T-W-2 HoG (Histogram of Gradients) descriptor. Augmenting HoG with integral images. Parameterization offeature space. 2

T-W-3Boosting as a learning meta-algorithm suitable for large-scale data and computer vision tasks.Properties of AdaBoost and RealBoost algorithms. Accuracy measures of detectors (sensitivity, FAR,ROC curves, AUC, F1). Cascades of classifiers.

7

Student workload - forms of activity Number of hoursAttendance at labs. 15A-L-1

Self-work (at home) on homework assignment. 75A-L-2

Attendance at lectures. 15A-W-1

Preparation to exam. 15A-W-2

Teaching methods / toolsM-1 Lectures in form of a presentation with mathematical derivations of needed algorithms.


S-1 Lectures - single-choice test.Laboratories - homework assiignment / project (face or body detector).F



study





education

Teachingmethods


methods

KnowledgeWM-WI_2-_null_W01Students will be familiar with techniques and algorithms relatedto detection of objects in digital images.

SkillsWM-WI_2-_null_U01Hands-on experience in implementation of object detector.


Computer Vision and Fast Object Detection

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3,03,54,04,55,0


3,03,54,04,55,0


Required reading1. B. Cyganek, Object Detection and Recognition in Digital Images: Theory and Practice, Wiley, 20132. P. Klęsk, Techniques of fast detection, [pdf presentation by lecturer], 2014,http://wikizmsi.zut.edu.pl/uploads/4/4a/Szybka_detekcja.pdf

[ logo uczelni ]

Field of study

Mode of study


ECTS


Code

Electives

Language

Elective group


stationary

WI-2-CVS

3,0

credits english

ECTS (forms) 3,0

Level second cycle

Area(s) of study


Module

Course unit Computer Vision for Video Surveillance







Other teachers

PrerequisitesW-1 Elementary digital image processing

W-2 Elementary numerical recipes

W-3 Elementary programming skills

W-4 Elementary matrix algebra


C-1 The main objective of the course is to familiarize students with the range of possibilities and principles of the modernintelligent monitoring systems.

C-2 Students will be prepared to design intelligent surveillance system performing the tasks of automatic processing, analysisand recognition of digital images.

Course content divided into various forms of instruction Number of hoursT-L-1 Introduction to laboratory classes. 1

T-L-2 Video surveillance at the Faculty and on the campus. The ALPR system. Image acquisition fromcameras. 2

T-L-3 Performance verification of available (ready to use, implemented) algorithms for video surveillance,e.g.: background modelling, object detection, object recognition, object tracking 4

T-L-4 Implementation of selected algorithms for video surveillance, e.g.: background modelling, objectdetection, object recognition, object tracking. 4

T-L-5 Development of a concept of simple video surveillance system. Definition of the scope of the project.Design and implementation of own simple video surveillance system. 4

T-W-1Introduction to video surveillance systems. Selected issues and classification of monitoring systems.Legal regulations. Systems of video-observation. Hardware in video monitoring systems. IntelligentBuilding. Intelligent cameras. Mobile wireless platforms. Access control controllers.

3

T-W-2 Thermal imaging for video observation. 1

T-W-3 Intelligent Transport Systems (ITS): ALPR, WIM, HIM, red-light, others. Measuring traffic congestion.Intelligent parking. 2

T-W-4 Background modeling methods. 2

T-W-5 Autoamtic detection and recognition of objects in video surveilance systems. 3

T-W-6 Tracking algorithms. 1

T-W-7 Example implementations of intelligent video surveillance systems: vehicle traffic measurementsystems, human traffic analysis, people identification based on biometric features, etc. 3





Computer Vision for Video Surveillance

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Student workload - forms of activity Number of hoursDesign and implementation of own video surveillance system. 19A-L-5










S-3 Progress monitoring in implementation of own video surveillance system.F




study





education

Teachingmethods


methods

KnowledgeWM-WI_2-_??_W01Students are familiarized with the computer vision methodsapplicable to video surveillance. Students are acquainted withprinciples of the modern intelligent monitoring systems.

SkillsWM-WI_2-_??_U01Students are prepared to design intelligent surveillance systemperforming the tasks of automatic processing, analysis andrecognition of digital images.

Other social / personal competencesWM-WI_2-_??_K01The student is aware of the role of video surveillance systemsfor the society.



3,03,54,04,55,0


3,03,54,04,55,0


3,03,54,04,55,0

Required reading1. H. Kruegle, CCTV Surveillance, Second Edition: Video Practices and Technology, Butterworth-Heinemann, 2006, 672 p.

2. R. Gonzalez, R. Woods, S. L. Eddins, Digital Image Processing Using MATLAB 2nd Ed., Gatesmark Publishing, 2009, 827 p.

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Supplementary reading1. J. S. Sussman, Perspectives on Intelligent Transportation Systems (ITS), Springer, 2005, 229 p.

[ logo uczelni ]

Field of study

Mode of study


ECTS


Code

Electives

Language

Elective group


stationary

WI-2-DAM

3,0

credits english

ECTS (forms) 3,0

Level second cycle

Area(s) of study


Module

Course unit Data Analysis and Machine Learning







Other teachers



W-3 programming

W-4 probability calculus and statistics

Module/course unit objectivesC-1 Developping a general understanding about data analysis and machine learning methods.

Course content divided into various forms of instruction Number of hoursT-L-1 Programming PCA in MATLAB. 3

T-L-2 Programming CART trees in MATLAB. 4

T-L-3 Programming SVM optimization tasks (several versions) in MATLAB. 4

T-L-4 Programming MARS algorithm in MATLAB. 4

T-W-1Principal Component Analysis (PCA) as a method for dimensionality reduction. Review of notions:variance, covariance, correlation coefficient, covariance matrix. Minimization of projection lengths ofdata points onto a given direction. Derivation of PCA. Interpretation of eigenvalues and eigenvectors.

3

T-W-2 Decision trees - CART algorithm. Impurity functions, greedy generation of a complete tree. Pruningheuristics for decision trees (depth-based, leaves-based). 3

T-W-3Support Vector Machines (SVM). Distance of data points from the decision hyperplane. Separationmargin. Formulation of the SVM optimization task without and with Lagrange multipliers. Supportvectors - what are they? Soft-margin SVM and related optimization tasks. SVMs with non-linear decisionboundary using the kernel trick.

5

T-W-4Multivariate Adaptive Regression Splines (MARS) for approximation tasks. Construction of splines.Least-squares approximation with arbitrary bases (in particular MARS splines). Learning algorithm.Similarities to CART.

2

T-W-5 Exam. 2


Programming homework assignments. 40A-L-2

Preparation for short tests conducted in the lab at the end of each topic. 4A-L-3





Data Analysis and Machine Learning

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Teaching methods / toolsM-2 Computer programming.




P




study





education

Teachingmethods


methods

KnowledgeWM-WI_2-_??_W01Student has an elementary knowledge on machine learningalgorithms and techniques suitable for data analysis.

SkillsWM-WI_2-_??_U01Student can implement (Python or MATLAB) algorithmspresented during lectures.




3,03,54,04,55,0


3,03,54,04,55,0


Required reading1. M. J. Zaki, W. Meira Jr, Data Mining and Analysis - Fundamental Concepts and Algorithms, Cambridge University Press, 2014


[ logo uczelni ]

Field of study

Mode of study


ECTS


Code

Electives

Language

Elective group


stationary

WI-2-DSY

6,0

credits english

ECTS (forms) 6,0

Level second cycle

Area(s) of study


Module

Course unit Database systems







Other teachers

Prerequisites

Module/course unit objectivesC-1 Student is able to desing and operate databese systems.

Course content divided into various forms of instruction Number of hoursT-L-1 Relational Database Modelling 6

T-L-2 Relational Database Programming 20

T-L-3 Relational Database Management 4

T-W-1 Worlds of database systems 2

T-W-2 Relational Database Modelling 4

T-W-3 Relational Database Programming 20

T-W-4 Relational Database Management 4


Homework 90A-L-2


Homework 30A-W-2

Teaching methods / toolsM-1 Invormative lectures




study





education

Teachingmethods


methods

KnowledgeWM-WI_2-_??_W01Student is able to descrive variuos types od database systems.

SkillsWM-WI_2-_??_U01Student is able to design and operate varions types of databesesystems.


Database systems

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3,03,54,04,55,0


3,03,54,04,55,0


Required reading1. Garcia-Molina, Ullman, Widom, Database Systems. The Complete Book, Pearson, Upper Saddle River, 2009

[ logo uczelni ]

Field of study

Mode of study


ECTS


Code

Electives

Language

Elective group


stationary

WI-2-DMA

4,0

credits english

ECTS (forms) 4,0

Level second cycle

Area(s) of study


Module

Course unit Data Mining Algorithms







Other teachers


W-2 programming


Module/course unit objectivesC-1 Building the understanding about learning from data.

C-2 Familiarization with probabilistic, tree-based, and boosted classifiers, and the related algorithms.

C-3 Familiarization with rules mining and related algorithms.


T-L-1 Programming the naive Bayes classifier (MATLAB) - for 'wine data set' (in class) and a selected data set(homework). 8

T-L-2 Programming the Apriori algorithm - mining association rules. 6

T-L-3 Programming an exhaustive generator of decision rules (for given premise length). 6

T-L-4 Programming the CART algorithm - building a complete tree. 4

T-L-5 Programming heuristics for pruning CART trees. 6

T-W-1Review of some elements of probability calculus. Derivation of Naive Bayes classifier. Remarks oncomputational complexity with and without the naive assumption. Bayes rule. LaPlace correction. Betadistributions.

4

T-W-2Mining association rules by means of Apriori algorithm. Support and confidence measures. Findingfrequent sets (induction). Rules generation mechanics. Remarks on the hashmap data structureapplied for Apriori algorithm. Pareto-optimal rules. Remarks on decision rules generation.

4

T-W-3Decision trees and CART algorithm. Impurity functions and their properties. Best splits as minimizers ofexpected impurity of children nodes. CART greedy algorithm. Tree pruning heuristics (by depth, bypenalizing number of leafs). Recursions for traversing the subtrees (greedy and exhaustive).

3

T-W-4 Ensemble methods: bagging and boosting (meta classifiers). AdaBoost algorithm. Exponential criterionvs zero-one-loss function. Real boost algorithm. 2

T-W-5 Exam. 2


Programming homework tasks. 24A-L-2

Preparation for short tests (15 min) carried out in lab classes. 6A-L-3




Data Mining Algorithms

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P




study





education

Teachingmethods


methods

KnowledgeWM-WI_2-_??_W01Student has an elementary knowledge on data miningalgorithms.

SkillsWM-WI_2-_??_U01Student can implement (Python or MATLAB) algorithmspresented during lectures.




3,03,54,04,55,0


3,03,54,04,55,0


Required reading1. M. J. Zaki, W. Meira Jr, "Data Mining and Analysis - Fundamental Concepts and Algorithms", Cambridge University Press, 2014


[ logo uczelni ]

Field of study

Mode of study


ECTS


Code

Electives

Language

Elective group


stationary

WI-2-DCM

6,0

credits english

ECTS (forms) 6,0

Level second cycle

Area(s) of study


Module

Course unit Digital color management







Other teachers

Prerequisites


C-1Upon successful completion of the course, the student will be able to:1. Identify problems from their specific domains suitable for simulation, and correctly approach the modeling of thoseproblems, including identification of simulation goals and necessary real-world data.2. Implement and execute discrete-event simulation models and correctly interpret and present the results.

Course content divided into various forms of instruction Number of hoursT-L-1 Monitor callibration 6

T-L-2 Input devices callibration 6

T-L-3 Printers callibration 18

T-W-1 Human colour reception 2

T-W-2 Standard colour spaces 2

T-W-3 Colour measurement 2

T-W-4 ICC profiles 3

T-W-5 Devices calibration 4

T-W-6 Colour Management System 2


Homework 90A-L-2


Homework 45A-W-2

Teaching methods / toolsM-1 Invormative lectures

Evaluation methods (F - progressive, P - final)S-1 Oral examP



study





education

Teachingmethods


methods

Knowledge

Digital color management

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WM-WI_2-_??_W01Upon successful completion of the course, the student will beable to:• Describe colour phenomena• Measure colour parameters using spectrophotometer

SkillsWM-WI_2-_??_U01Student will be able to:• Apply various colour spaces (CIE LAB, CIE XYZ, CIE xyY, CIELUV, RGB, CMYK)• Use ICC profiles in a color workwlow• Organize a reliable colour management system




3,03,54,04,55,0


3,03,54,04,55,0


Required reading1. Fraser, B., C. Murphy, F. Bunting, Real World Color Management, Peachpit Press, 2004

2. Sharma, A., Understanding Color Management, Delmar Cengage Learning, 2003

[ logo uczelni ]

Field of study

Mode of study


ECTS


Code

Electives

Language

Elective group


stationary

WI-2-EEG

4,0

credits english

ECTS (forms) 4,0

Level second cycle

Area(s) of study


Module

Course unit EEG signal analysis in Matlab

















T-L-8 Exam. 2





T-W-5 Exam. 2






M-2 Discussion.






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study





education

Teachingmethods


methods

Knowledge

C-1 S-2

T-L-7T-W-1

M-1M-2


T-W-2T-W-3

Skills

C-1 S-1T-L-1T-L-2T-L-3T-L-4


T-L-5T-L-6T-L-7





3,54,04,55,0



3,54,04,55,0




[ logo uczelni ]

Field of study

Mode of study


ECTS


Code

Electives

Language

Elective group


stationary

WI-2-ESY

4,0

credits english

ECTS (forms) 4,0

Level second cycle

Area(s) of study


Module

Course unit Expert systems







Other teachers

PrerequisitesW-1 Algorithms and data structures

Module/course unit objectivesC-1 To learn the basic knowledge in expert systems. Student will have the ability to recognize areas of implementation.

C-2 Students will be able to design, build and implement rule-based expert systems.

Course content divided into various forms of instruction Number of hoursT-L-1 CLIPS - installing and dealing with facts 2

T-L-2 Rules constract in CLIPS 4

T-L-3 Excerises with simple user interface communication in CLIPS 6

T-L-4 Functions and advanced CLIPS programming 6

T-L-5 Project in CLIPS 5

T-L-6 From CLIPS to JESS 7

T-W-1 History of Expert Systems. The begining, early solutions. 2

T-W-2 Fomal representation of knowladge in expert systems. Dealing with uncertainty. 2

T-W-3 Propositional logic as a method of knowladge representation. 2

T-W-4 First predicate logic. Prolog programming language. 3

T-W-5 Uncetrainty - probablistic view. Bayes theorem and bayesian networks. 2

T-W-6 Fuzzy expert systems. 2

T-W-7 Expert systems based on certainty factor. 2


Study the literature 20A-L-2

Working on homeworks 34A-L-3


Study the literature 15A-W-2

Preparing for test 6A-W-3

Teaching methods / toolsM-1 Presentation, lecture

M-2 Discussion durig lecture.

M-3 Developing software in CLIPS

Expert systems

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Evaluation methods (F - progressive, P - final)S-1 Test checking the knowledge on expert systemsP

S-2 Short programming tasks in CLIPSF

S-3 Programming project - make your own expert systemP



study





education

Teachingmethods


methods

Knowledge

C-1 S-1


M-1M-2

WM-WI_2-_null_W01Student understand a structure of the expert system. Has aknowladge on representation forms and how the uncertatintycould be represented. Can name and explain how well-knownexpert systems work.

T-W-5T-W-6T-W-7

Skills

C-2 S-2S-3

T-L-1T-L-2T-L-3

M-3WM-WI_2-_null_U01Students has the ability to develop expert systems in CLIPS andJESS.

T-L-4T-L-5T-L-6




3,0 Basic knowledge on expert systems.

3,54,04,55,0


3,03,54,04,55,0


Required reading1. Russel S., Norvig P, Artificial Intelligence A modern approach, Prentice Hall, 2003

2. Clips online documentation, 2016

[ logo uczelni ]

Field of study

Mode of study


ECTS


Code

Electives

Language

Elective group


stationary

WI-2-FPL

4,0

credits english

ECTS (forms) 4,0

Level second cycle

Area(s) of study


Module

Course unit F# Programming Language







Other teachers


Module/course unit objectivesC-1 Familiar with the sytnax, structures and principles used in the f# language

C-2 The ability to develop a program in f# language.

Course content divided into various forms of instruction Number of hoursT-L-1 Introduction to visual Studio IDE and F# 2

T-L-2 Declaring values and functions, pattern matching basics 2

T-L-3 Recursive and higher order functions 2

T-L-4 Option types, tuples and records 2

T-L-5 Lists and sequences 4

T-L-6 Sets, maps and discriminated unions 2

T-L-7 Control flows 2

T-L-8 Arrays 2

T-L-9 Mutable data and mutable collections 2

T-L-10 I/O operations 2

T-L-11 Classes and operator overloding 2

T-L-12 Inheritance and interfaces 2

T-L-13 F# advanced 4

T-W-1 Introduction to: Functional Programming and F# programming language 2

T-W-2 Working With Functions 2

T-W-3 Immutable Data Structures 4

T-W-4 Imperative Programming 2

T-W-5 Object Oriented Programming 2

T-W-6 F# Advanced 2

T-W-7 Exam 1




F# Programming Language

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Student workload - forms of activity Number of hoursStudent individual work 15A-W-2


M-2 Discussion



S-2 written examP



study





education

Teachingmethods


methods

Knowledge

C-1 S-2T-W-1T-W-2T-W-3

M-1M-2

WM-WI_2-_null_W01After the lecture the student will know the f# syntax and will beable to define programming concepts used in the f# language.

T-W-4T-W-5T-W-6

C-2 S-2T-W-1T-W-2T-W-3

M-1M-2

WM-WI_2-_null_W02After the lecture the student will be able to explain what ishappening in a f# code.

T-W-4T-W-5T-W-6

Skills

C-1C-2 S-1


M-2M-3

WM-WI_2-_null_U01The student will be able to write program in a f# language.

T-L-8T-L-9T-L-10T-L-11T-L-12T-L-13




3,0 The student knows f# syntax.

3,54,04,55,0

WM-WI_2-_null_W02 2,03,0 The student is able to explain code of a simple program written in f#.

3,54,04,55,0


3,0 The student is able to write a simple program in a f# language.

3,54,04,55,0


Required reading1. Robert Pickering, Beginning F#, 2009

2. Don Syme, Adam Granicz, Antonio Cisternino, Expert F#, 2007

Supplementary reading1. Jon Harrop, F# for Scientists, 2008

2. https://en.wikibooks.org/wiki/F_Sharp_Programming

[ logo uczelni ]

Field of study

Mode of study


ECTS


Code

Electives

Language

Elective group


stationary

WI-2-FEC

3,0

credits english

ECTS (forms) 3,0

Level second cycle

Area(s) of study


Module

Course unit Fundamentals of Error-Correcting Block Codes






Majorkowska-Mech Dorota ([email protected])Leading teacher

Other teachers

PrerequisitesW-1 Basics of linear and abstract algebra.

Module/course unit objectivesC-1 Knowledge and skills in error-correcting codes construction.

C-2 Knowledge of error-correcting codes

C-3 Skills in error-correcting codes construction

Course content divided into various forms of instruction Number of hoursT-A-1 Calculation of the minimum distance, detection and correction capability of line code codes. 2

T-A-2 Examination of the properties of algebraic structures. 2

T-A-3 Construction of extended Galois fields. 2

T-A-4 Finding primitive elements of extended Galois fild, minimal polynomials and conjugates of elements. 2

T-A-5 Linear block codes: matrix description, standard array, syndrome. Constructing of Hamming codes. 2

T-A-6 Cyclic codes: polynomial and matrix description of cyclic codes, encoding, syndrome computation,error detection and decoding. Constructing some examples of cyclic codes. 3

T-A-7 Written test. 2

T-W-1 The discrete communication channel. Types of errors and types of error-correcting codes.Block codes, minimum distance, error-detecting and error-correcting capabilities of a block code. 2

T-W-2 Algebraic structures: groups, rings, fields, vector spaces. 2

T-W-3 Construction of extended Galois fields. 2

T-W-4 Structure of extended Galois fields, primitive elements, minimal polynomials and conjugates. 2

T-W-5 Linear block codes: matrix description, standard array, syndrome, Hamming codes, Hamming spheresand perfect codes. 2

T-W-6 Cyclic codes: polynomial and matrix description of cyclic codes, encoding, syndrome computation,error detection and decoding. Important classes of cyclic codes. 4

T-W-7 Written exam. 1

Student workload - forms of activity Number of hoursparticipations in classes 15A-A-1

homeworks 15A-A-2

self study 15A-A-3

participations in lectures 15A-W-1

self study 30A-W-2


Fundamentals of Error-Correcting Block Codes

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Teaching methods / toolsM-2 Solving problems on board (workshop)


S-2 Written testP



study





education

Teachingmethods


methods

Knowledge

C-2 S-1M-1WM-WI_2-_null_W01Students has knowledge in error-correcting codes construction

Skills

C-3 S-2M-2WM-WI_2-_null_U01Students has skills in error-correcting codes construction




3,03,54,04,55,0


3,03,54,04,55,0


Required reading1. Richard E. Blahut, Algebraic Codes for Data Transmission, Cambridge University Press, New York, 2003

Supplementary reading1. Shu Lin, Daniel J. Costello, Error Control Coding: Fundamentals and Applications, Pearson-Prentice Hall, 2004

[ logo uczelni ]

Field of study

Mode of study


ECTS


Code

Electives

Language

Elective group


stationary

WI-2-GUI

3,0

credits english

ECTS (forms) 3,0

Level second cycle

Area(s) of study


Module

Course unit Graphical User Interface in .NET







Other teachers


Module/course unit objectivesC-1 Familiar with Windows Forms and Windows Presentation Foundation

C-2 The ability to develop Windows Form Application and Windows Presentation Foundation Application.

Course content divided into various forms of instruction Number of hoursT-L-1 Introduction to Windows Forms 1

T-L-2 Controls, Forms, Containers and Applications, Menus, Toolbars, Dialogs 2

T-L-3 Settings, Resources 2

T-L-4 Building Controls, Inheritance and Reuse, Property Grids, Data binding 2

T-L-5 Introduction to Windows Presentation Foundation 1

T-L-6 XAML 1

T-L-7 Sizing, Positioning and Transforming Elements, Layout with Panels 2

T-L-8 Input Events, Content Controls, Item Controls 2

T-L-9 Image, Text, Other Controls, Resources, Data Binding 2

T-W-1 Windows Forms Fundamentals 2

T-W-2 Custom Controls 2

T-W-3 Modern Controls 2

T-W-4 Data Binding and Windows Forms Techniques 2

T-W-5 Building a WPF Application 2

T-W-6 WPF Controls 2

T-W-7 Data Binding and Rich Media 2

T-W-8 Exam 1






Graphical User Interface in .NET

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Teaching methods / toolsM-2 Discussion



S-2 written examP



study





education

Teachingmethods


methods

Knowledge

C-1 S-2T-W-1T-W-2 M-1

M-2WM-WI_2-_null_W01After the course the student will possess knowledge aboutWindows Forms

T-W-3T-W-4

C-1 S-2T-W-5T-W-6 M-1

M-2WM-WI_2-_null_W02After the course the student will possess knowledge aboutWindows Presentation Foundation

T-W-7

Skills

C-2 S-1T-L-1T-L-2 M-2

M-3WM-WI_2-_null_U01After the course students will be able to design and createWindows Form Application

T-L-3T-L-4

C-2 S-1T-L-5T-L-6T-L-7

M-2M-3

WM-WI_2-_null_U02After the course students will be able to design and createWindows Presentation Foundation Application.

T-L-8T-L-9




3,0 The student possesses basic knowledge about Windows Forms and common controls

3,54,04,55,0

WM-WI_2-_null_W02 2,03,0 The student possesses basic knowledge about Windows Presentation Foundation

3,54,04,55,0


3,0 The student will be able to write a simple Windows Form Application

3,54,04,55,0

WM-WI_2-_null_U02 2,03,0 The student will be able to write a simple Windows Presentation Foundation Application

3,54,04,55,0


Required reading1. Chris Sells, Windows Forms Programming in C#, 2003

2. Matthew MacDonald, Pro .NET 2.0 Windows Forms and Custom Controls in C#, 2005

3. Adam Nathan, WPF 4.5 Unleashed, 2013

Supplementary reading1. Andrew Troelsen, Philip Japikse, C# 6.0 and the .NET 4.6 Framework, 2015

[ logo uczelni ]

Field of study

Mode of study


ECTS


Code

Electives

Language

Elective group


stationary

WI-2-HMM

3,0

credits english

ECTS (forms) 3,0

Level second cycle

Area(s) of study


Module

Course unit Hidden Markov models and its applications







Other teachers

PrerequisitesW-1 Basics of statistics and higher mathematics.

W-2 Basic knowledge about artificial intelligence.

Module/course unit objectivesC-1 Theoretical knowledge about Markov Models.

C-2 The ability to apply Markov Models in patter recognition tasks.

Course content divided into various forms of instruction Number of hoursT-L-1 Solving pattern recognition problems with Markov Chain 2

T-L-2 Developing own implementation of Markov Chain 2

T-L-3 Solving pattern recognition problems with Hidden Markov Model 5

T-L-4 Developing own implementation of Hidden Markov Model 4

T-L-5 Solving pattern recognition problem with Continuous Observation Densities HMM 2

T-W-1 Intorduction to Markov Models, "Observable" Markov Model 2

T-W-2 Fundamentals of Hidden Markov Models (HMM) 2

T-W-3 Forward-backward algorithm, Viterbi Algorithm 2

T-W-4 Baum-Welch Reestimation method 2

T-W-5 Implementation issues for HMM: variables scaling, multiple observations sequences 2

T-W-6 Continuous Observation Densities in HMM 2

T-W-7 Mixture HMM 2

T-W-8 Exam 1






M-2 Discussion



Hidden Markov models and its applications

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S-2 written examP



study





education

Teachingmethods


methods

Knowledge

C-1 S-2T-W-1T-W-2T-W-3T-W-4

M-1M-2

WM-WI_2-_null_W01After the course the student will possess knowledge about theconstruction, internal algorithms and applications of MarkovModels, Hidden Markov Models and its modifications.

T-W-5T-W-6T-W-7

Skills

C-2 S-1T-L-1T-L-2T-L-3

M-2M-3

WM-WI_2-_null_U01After the course students will be able to make use of HMM inpatter recognition tasks.

T-L-4T-L-5




3,0 The student possesses basic knowledge about Markov Models, internal algorithms and its applications.

3,54,04,55,0


3,0 The student is able to use Markov Models in a simple pattern recognition task.

3,54,04,55,0


Required reading1. Ming Liao, Applied Stochastic Processes, 2013

2. Andrew M. Fraser, Hidden Markov Models and Dynamical Systems, 2008

3. Gernot A. Fink, Markov Models for Pattern Recognition: From Theory to Applications, 2008

[ logo uczelni ]

Field of study

Mode of study


ECTS


Code

Electives

Language

Elective group


stationary

WI-2-HCI

3,0

credits english

ECTS (forms) 3,0

Level second cycle

Area(s) of study


Module

Course unit Human-Computer Interaction







Other teachers

PrerequisitesW-1 Elementary programming skills


C-1 The main objective of the course is to familiarize students with the current trends in human-computer interaction. Newapproaches like touchless interaction as well as classical methods are discussed and analyzed during the course.

C-2 Students are familiarized with the wide range of modern equipment, software and algorithms of human-computerinteraction.


T-L-1


15

T-W-1


15





Design and implementation of own human-computer interaction system. 19A-L-5







Human-Computer Interaction

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Evaluation methods (F - progressive, P - final)S-1 Final grade based on continuous assessment of tasks carried out during the classes.P




study





education

Teachingmethods


methods

KnowledgeWM-WI_2-_??_W01Students are familarized with the current trends in human-computer interaction. They gain knowledge about newapproaches like touchless interaction as well as classicalmethods.

SkillsWM-WI_2-_??_U01Students are familiarized with the wide range of modernequipment, software and algorithms of human-computerinteraction.

Other social / personal competencesWM-WI_2-_??_K01Student has the consciousness of building communicationsystems in the strict connection with a social group that is theaddressee of the given solutions (culture, norms, status).Student is aware of the responsibility for the wronginterpretation of the communication message.



3,03,54,04,55,0


3,03,54,04,55,0


3,03,54,04,55,0

Required reading1. A. Dix, J. Finlay, G. D. Abowd, R. Beale, Human-Computer Interaction, Pearson, 2004, 3rd Edition2. B. Shneiderman, C. Plaisant, Designing the User Interface: Strategies for Effective Human-Computer Interaction, Pearson Addison-Wesley, 2009, 5th Edition3. D. K. Kumar, S. P. Arjunan, Human-Computer Interface Technologies for the Motor Impaired, CRC Press, 2015

[ logo uczelni ]

Field of study

Mode of study


ECTS


Code

Electives

Language

Elective group


stationary

WI-2-IDS

6,0

credits english

ECTS (forms) 6,0

Level second cycle

Area(s) of study


Module

Course unit Intelligent Decision Systems









Module/course unit objectivesC-1 To provide the knowledge about multi-criteria decision-making methods which are used to solving decision problems

C-2 To equip the students with the ability of solving decision problems by using MCDM methods

Course content divided into various forms of instruction Number of hoursT-L-1 Intro to solving decision problems by using WSM and WPM methods 2

T-L-2 Intro to solving decision problems by using TOPSIS methods 4

T-L-3 Intro to solving decision problems by using AHP methods 5

T-L-4 Intro to solving decision problems by using ELECTRE methods 4

T-L-5 Intro to solving decision problems by using ANP methods 4

T-L-6 Intro to solving decision problems by using Fuzzy Logic 10

T-L-7 Exam 1

T-W-1 Description of decision making problems (structure, elements etc.) 3

T-W-2 Review of the MCDM methods (achievements and main directions of researches) 3

T-W-3 The WSM and WPM methods (examples, application, benefits, defects, etc.) 2

T-W-4 The AHP and ANP methods (examples, application, benefits, defects, etc.) 6

T-W-5 The ELECTRE methods (examples, application, benefits, defects, etc.) 4

T-W-6 The TOPSIS methods (examples, application, benefits, defects, etc.) 4

T-W-7 The Fuzzy methods in decision-making (examples, application, benefits, defects, etc.) 7

T-W-8 Exam 1






M-2 Discussion


Intelligent Decision Systems

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S-2 Written examP



study





education

Teachingmethods


methods

Knowledge

C-1C-2 S-2


M-1M-2

WM-WI_2-_null_W01After the lectures the student will be able to define a MCDMproblem, describe main MCDM methods, and choose the methodsuitable for a decision problem

T-W-5T-W-6T-W-7

Skills

C-2 S-1T-L-1T-L-2T-L-3

M-2M-3

WM-WI_2-_null_U01The student will be able to choose MCDM method for a problem.

T-L-4T-L-5T-L-6

C-2 S-1T-L-1T-L-2T-L-3

M-2M-3

WM-WI_2-_null_U02The student will be able to solve a multi-criteria problem.

T-L-4T-L-5T-L-6




3,0 The student is able to define the MCDM methods and problems concept

3,54,04,55,0


3,0 The student is able to choose an appropriate MCDM method to a specific decision problem

3,54,04,55,0

WM-WI_2-_null_U02 2,03,0 The student is able to solve a specific decision problem

3,54,04,55,0



[ logo uczelni ]

Field of study

Mode of study


ECTS


Code

Electives

Language

Elective group


stationary

WI-2-IMP

6,0

credits english

ECTS (forms) 6,0

Level second cycle

Area(s) of study


Module

Course unit Intro to Mathematical Programming









Module/course unit objectivesC-1 The course introduces to techniques for solving optimization tasks based on mathematical programming methods

Course content divided into various forms of instruction Number of hoursT-L-1 Linear programming: geometric method 4

T-L-2 Linear programming: simplex algorithm 4

T-L-3 Transportation theory: transport task 6

T-L-4 Program Evaluation and Review Technique (PERT) 5

T-L-5 Critical Path Method (CPM) 5

T-L-6 Traveling salesman problem: computing a solution 5

T-L-7 Exam 1

T-W-1 Intro to linear programming 7

T-W-2 Applications of linear programming 2

T-W-3 Intro to transportation theory 6

T-W-4 Applications of transportation theory 2

T-W-5 Intro to network Programming 6

T-W-6 Applications of network programming 2

T-W-7 Traveling salesman problem 4

T-W-8 Exam 1






M-2 Discussion



Intro to Mathematical Programming

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S-2 Written examP



study





education

Teachingmethods


methods

Knowledge

C-1 S-2


M-1M-2

WM-WI_2-_null_W01After the lectures the student will be able to define and descrbe:-linear programming methods and problems,-transportation task methods and problems,-network programming methods and problems,-traveling salesman problem.

T-W-5T-W-6T-W-7

Skills

C-1 S-1T-L-1T-L-2T-L-3

M-2M-3

WM-WI_2-_null_U01The student will be able to use the methods which will bepresented on the laboratories

T-L-4T-L-5T-L-6




3,0 The student has to define and describe methods and problems presented on the lectures

3,54,04,55,0


3,0 The student is able to use the methods which were presented on the laboratories

3,54,04,55,0



[ logo uczelni ]

Field of study

Mode of study


ECTS


Code

Electives

Language

Elective group


stationary

WI-2-IST

6,0

credits english

ECTS (forms) 6,0

Level second cycle

Area(s) of study


Module

Course unit Intro to Statistic: Making Decisions Based on Data







Other teachers



C-1 The course introduces to techniques for visualizing relationships in data and systematic techniques for understanding therelationships using mathematics.


T-L-1 Visualizing relationships in data (seeing relationships in data and predicting based on them,simpson's paradox, etc.) 4

T-L-2 Probability (Bayes Rule, correlation vs. causation, etc.) 5

T-L-3 Estimation (maximum likelihood estimation, mean, median, mode, standard deviation, variance, etc.) 5

T-L-4 Outliers and normal distribution (outliers, quartiles, binomial distribution, central limit theorem,manipulating normal distribution, etc.) 5

T-L-5 Inference (confidence intervals, hypothesis testing, etc.) 5

T-L-6 Regression (linear regression, correlation, etc.) 5

T-L-7 Exam 1

T-W-1 Visualizing relationships in data (seeing relationships in data and predicting based on them, simpson'sparadox, etc.) 4

T-W-2 Probability (Bayes Rule, correlation vs. casuation, etc.) 5

T-W-3 Estimation (maximum likelihood estimation, mean, median, mode, standard deviation, variance, etc.) 5

T-W-4 Outliers and normal distribution (outliers, quartiles, binomial distribution, central limit theorem,manipulating normal distribution, etc.) 5

T-W-5 Inference (confidence intervals, hyphotesis testing, etc.) 5

T-W-6 Regression (linear regression, correlation, etc.) 5

T-W-7 Exam 1

Student workload - forms of activity Number of hoursThe attendance in the lectures 30A-L-1





M-2 Discussion


Intro to Statistic: Making Decisions Based on Data

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S-2 Written examP



study





education

Teachingmethods


methods

Knowledge

C-1 S-2T-L-1T-L-2T-L-3

M-1M-2

WM-WI_2-_null_W01After the lectures the student will be able to define and describepresented statistical techniques and measures

T-L-4T-L-5T-L-6

Skills

C-1 S-1M-2M-3

WM-WI_2-_null_U01The student will be able to calculate and use the main statisticalmeasures and techniques




3,0 The student is able to define and describe the main statsistical measures and techniques

3,54,04,55,0


3,0 The student is able to calculate and use statistical measures and techniques

3,54,04,55,0



[ logo uczelni ]

Field of study

Mode of study


ECTS


Code

Electives

Language

Elective group


stationary

WI-2-KEO

4,0

credits english

ECTS (forms) 4,0

Level second cycle

Area(s) of study


Module

Course unit Knowledge Engineering and Ontology Development







Other teachers


Module/course unit objectivesC-1 Familiar with the syntax, structures and principles used in OWL language

C-2 The ability to design and write small-scale ontologies and to use reasoning mechanisms

Course content divided into various forms of instruction Number of hoursT-L-1 Introduction to the ontologies 2

T-L-2 Protégé ontology editor and OWL language 3

T-L-3 Building an OWL ontology: defining class hierarchy 4

T-L-4 OWL object property characteristics 2

T-L-5 Building an OWL ontology: defining individuals, data type properties 4

T-L-6 Graphical visualization of the ontology 2

T-L-7 Describing and defining classes 3

T-L-8 The application of reasoning mechanisms and query tools 5

T-L-9 The application of plugins and tools to manage the ontology 5

T-W-1 Introduction to the ontologies 2

T-W-2 Ontology editors and standards for ontology description 3

T-W-3 Selected approaches to the ontology construction and knowledge engineering methods 2

T-W-4 Building an OWL ontology 6

T-W-5 Primitive and defined classes 4

T-W-6 Selected reasoning mechanisms and Open World Reasoning 4

T-W-7 Reusing of existing ontologies 2

T-W-8 Creating other OWL constructs in Protégé 2

T-W-9 Restriction types 2

T-W-10 Ontology-based solutions to knowledge extraction 2

T-W-11 Exam 1





Knowledge Engineering and Ontology Development

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M-2 Discussion



S-2 Project workF



study





education

Teachingmethods


methods

KnowledgeWM-WI_2-_??_W01After the course the student should be able to understand anduse the basic ontology constructs in OWLWM-WI_2-_??_W02After the course the student should be able to design andconstruct a small-scale ontology

Skills




3,03,54,04,55,0

WM-WI_2-_??_W02 2,03,03,54,04,55,0

Skills


Required reading1. Michael K. Smith, Chris Welty, and Deborah L. McGuinness, OWL Web Ontology Language Guide, 2004, http://www.w3.org/TR/owl-guide/1. Matthew Horridge (eds.), A Practical Guide To Building OWL Ontologies Using Protege 4 and CO-ODE Tools Edition 1.2, The Universityof Manchester, Manchester, 20092. Protege tutorial. Available from http://protege.stanford.edu/

Supplementary reading2. Natalya F. Noy and Deborah L. McGuinness, Ontology Development 101: A Guide to Creating Your First Ontology, Stanford KnowledgeSystems Laboratory Technical Report, 2001

[ logo uczelni ]

Field of study

Mode of study


ECTS


Code

Electives

Language

Elective group


stationary

WI-2-LAT

2,0

credits english

ECTS (forms) 2,0

Level second cycle

Area(s) of study


Module

Course unit LaTeX – document preparation system for engineers







Other teachers




T-L-1


15

T-W-1


15






Exam 2A-W-3




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study





education

Teachingmethods


methods

Knowledge

C-1 S-1T-W-1

M-1WM_2-_null_W01Student has knowledge about typesetting engineeringdocuments with LaTeX system

Skills

C-1 S-2T-L-1

M-2WM_2-_null_U01Student has practical skills in typesetting of engineeringdocuments with LaTeX system





3,54,04,55,0



3,54,04,55,0




[ logo uczelni ]

Field of study

Mode of study


ECTS


Code

Electives

Language

Elective group


stationary

WI-2-MBV

3,0

credits english

ECTS (forms) 3,0

Level second cycle

Area(s) of study


Module








Prerequisites









homework 30A-W-2



exam 3A-W-5






study





education

Teachingmethods


methods

KnowledgeWM-WI_2-_??_W01Student understands communication principles.




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WM-WI_2-_??_K01Student values the importance of communication in business.




3,54,04,55,0



3,54,04,55,0



3,54,04,55,0

[ logo uczelni ]

Field of study

Mode of study


ECTS


Code

Electives

Language

Elective group


stationary

WI-2-MAT

6,0

credits english

ECTS (forms) 6,0

Level second cycle

Area(s) of study


Module

Course unit MATLAB Programming







Other teachers


Module/course unit objectivesC-1 Acquire the basic knowledge on Matlab programming

C-2 The practical skills of the Matlab programming

Course content divided into various forms of instruction Number of hoursT-L-1 Practical exercises of program content 29

T-L-2 Exam 1

T-W-1 Create Scripts 6

T-W-2 Create Live Scripts 2

T-W-3 Loop Control Statements 6

T-W-4 Conditional Statements 6

T-W-5 Add Comments to Programs 2

T-W-6 Run Code Sections 2

T-W-7 Run Sections in Live Scripts 3

T-W-8 Scripts vs. Functions 2

T-W-9 Exam 1






M-2 Discussion



S-2 Written examP

MATLAB Programming

[ logo uczelni ]



study





education

Teachingmethods


methods

Knowledge

C-1 S-2T-W-1T-W-2T-W-3T-W-4

M-1M-2

WM-WI_2-_null_W01After the lectures the student will be able to define and describeconcepts of Scripts, Live Scripts, Loop Control Statements,Conditional Statements


Skills

C-2 S-1T-L-1 M-2

M-3WM-WI_2-_null_U01The student will be able to write a simple program in the Matlabenviorment




3,0 The student is able to define and describe the basic concepts of the Matlab programming

3,54,04,55,0


3,0 The student is able to write a simple program in the Matlab enviorment

3,54,04,55,0



[ logo uczelni ]

Field of study

Mode of study


ECTS


Code

Electives

Language

Elective group


stationary

WI-2-MIG

3,0

credits english

ECTS (forms) 3,0

Level second cycle

Area(s) of study


Module

Course unit Methods of Artificial Intelligence in Games







Other teachers



W-3 programming

W-4 object-oriented programming

W-5 introduction to artificial intelligence


C-1 Familiarization with elements of the game theory (in particular, the notions of optimal mixed strategy and Nashequilibrium).

C-2 Demonstration of advanced techniques for searching game trees (perfect information games).

C-3 Getting familiar with algorithms for games with random elements or imperfect information games.

C-4 Demonstrating possible algorithms for controling agents in unknown environments.


T-L-1 Formulating the homework project - "Cops and thief": rules of the game, details of environment(engine), planning the tournament for future programs written by students. 3

T-L-2 A trial round of playing "Cops and thief", testing the environment. 3

T-L-3 The actual "Cops and thief" tournament (students' programs competing against one another). 3

T-L-4 Homework project "Hay" (take as few tricks as possible) - a card game of imperfect information.Details of environment for students' programs. 2

T-L-5 A trial round of play for "Hay" game. 2

T-L-6 The actual "Hay" game tournament (students' programs competing against one another). 2

T-W-1 Stentz algorithm (D*) for the shortest (cheapest) path in an unknown environment. 2

T-W-2

Selected elements of the game theory: game, strategy, finite game, zero-sum game, minimaxtheorem, dominated choices, unstable solution, optimal mixed strategy, Nash equilibrium (NEQ),Braess paradox. Game tree search problems. Measures of games complexities. Chinook project forcheckers. Algorithms for games of perfect information. MIN-MAX, alpha-beta pruning (fail-hard, fail-softversions). Knuth-Moore theorem.

3

T-W-3Quiescence algorithm. Moves sorting for alpha-beta pruning. Refutation table, killer heuristics.Transposition table. Progressive search. Zero-windows (scout windows) in searches. Scout algorithm.Negamax and Negascout variants. Remarks on genetic algorithms applied for discovering goodheuristics. Double-dummy problem in bridge.

2

T-W-4Games of perfect information with random elements. Expectiminimax algorithm. Backgammonexample. Games of imperfect information. Monte Carlo methods for generation of alternate trees andscores averaging.

2

T-W-5 Reinforcement learning. Definition of V and Q functions (example). Searching for optimal strategy.Bellman equations. Algorithms: TD, Q-learning. 2

T-W-6 Exam. 2

Methods of Artificial Intelligence in Games

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Programming (in pairs) AIs for homework projects: "Cops and thief" game, and "Hay" game. 45A-L-2

Participation in lectures and discussions. 15A-W-1

Self-preparation for final exam. 15A-W-2


M-2 Case study methods.

M-3 Didactic games.

M-4 Discussion.

M-5 Demonstration.


Evaluation methods (F - progressive, P - final)S-1 Two grades for homework projects (programmed AIs for games).F

S-2 Exam grade.P

S-3 Final grade as the mean from lab grade and exam grade.P



study





education

Teachingmethods


methods

Knowledge

C-1C-2C-3C-4

S-2M-1M-4M-5

WM-WI_2-_null_W01Student understands elementary notions from game theory andadvanced search algorithms. Student knows some techniquesapplicable in games of imperfect information and with randomelements.

SkillsC-1C-2C-3C-4

S-2M-1M-2M-4M-5

WM-WI_2-_null_U01Student can design and implement suitable AI solutions that canbe applied in games.




3,0 Uzyskanie przynajmniej 50% punktów z kolokwium końcowego. Kolokwium sprawdza rozumienie pojęć i algorytmówprzedstawionych w trakcie wykładów.

3,54,04,55,0


3,0Zaprogramowanie w ramach dwuosobowego zespołu dwóch programów - sztucznych inteligencji - pozwalających na wzięcieudziału w turnieju programów studentów z całej grupy. Programy powinny grać zgodnie z postawionymi regułami gry iwykazywać podstawowe inteligentne zachowania.

3,54,04,55,0


Required reading1. D.E. Knuth, R.W. Moore, An Analysis of Alpha-Beta Pruning, Artificial Intelligence, 1975

2. A. Reinefeld, An Improvement to the Scout Tree Search Algorithm, ICCA Journal, 1983

3. P. Klęsk, Electronic materials from: http://wikizmsi.zut.edu.pl, 2015

Supplementary reading1. J. von Neuman, O. Morgenstern, Theory of Games and Economic Behavior, 1944

2. D. Laramee, Chess Programming, 2011, tomy I-V

[ logo uczelni ]

[ logo uczelni ]

Field of study

Mode of study


ECTS


Code

Electives

Language

Elective group


stationary

WI-2-PPA

4,0

credits english

ECTS (forms) 4,0

Level second cycle

Area(s) of study


Module

Course unit Prolog Programming for Artifcial Intelligence







Other teachers

PrerequisitesW-1 The course does not require any previous knowledge

Module/course unit objectivesC-1 Knowledge in Prolog programming and the ability to recognize different algorithms from Artificial Inteligence

C-2 Ability to implement some (search, reasoning, inductive programming, belief networks) AI algoritghm using Prologprogramming language

Course content divided into various forms of instruction Number of hoursT-L-1 Simple example - facts and rules 2

T-L-2 Declarative and procedural meaning 2

T-L-3 Operators and arithmetic 2

T-L-4 Lists in Prolog 4

T-L-5 Eight queens problem solution 2

T-L-6 Cut, negation and backtracking 2

T-L-7 Build in predicates 2

T-L-8 Debugging 2

T-L-9 Tree and graph representation and search 4

T-L-10 Expert systems (if then) 4

T-L-11 Minimax - game playing 4

T-W-1 From First predicate logic to Prolog 3

T-W-2 Prolog syntax, lists, operators, arithmetics 2

T-W-3 Backtracking and build in predicates 2

T-W-4 Program examples - search blind and informed 2

T-W-5 Expert systems in Prolog 3

T-W-6 Game playing 3


Homeworks 34A-L-2

Studing the literatrue 20A-L-3


Self studying literature 15A-W-2

Studying for test 6A-W-3

Prolog Programming for Artifcial Intelligence

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Teaching methods / toolsM-1 Lecture, presentation

M-2 Discussion, learning by doing

M-3 Software developing in Prolog

Evaluation methods (F - progressive, P - final)S-1 Short programming tasksF

S-2 Writing exam or test from knowledge representation and Prolog.P



study





education

Teachingmethods


methods

Knowledge

C-1 S-2T-W-1T-W-2T-W-3

M-1M-2

WM-WI_2-_null_W01Explain the logic programming paradigm. Understand theresoninig in Prolog. Represent knowledge in First Predicate Logicand Prolog syntax.

T-W-4T-W-5T-W-6

Skills

C-2 S-2


M-2M-3

WM-WI_2-_null_U01Develop a given algorithm in Prolog using build-in and ownpredicates. Debug the Prolog code. Describe how the result isobtained.

T-L-7T-L-8T-L-9T-L-10T-L-11




3,0 Basic knowladge in Predicate Logic and Prolog

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3,0 Understanding examples from laboratories and implement them.

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Required reading1. Ivan Bratko, Prolog programming for Artificial Intelligence, Pearson Education, 2001

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Field of study

Mode of study


ECTS


Code

Electives

Language

Elective group


stationary

WI-2-SEN

4,0

credits english

ECTS (forms) 4,0

Level second cycle

Area(s) of study


Module

Course unit Software engineering







Other teachers

PrerequisitesW-1 Basic knowledge and skills in object-oriented programming, relational databases.

Module/course unit objectivesC-1 Possess knowledge and obtain practical skills in developing main products of software engineering process.

C-2 Usage of techniques and tools for development process where outcomes from one stage flow to subsequent stages.


Course content divided into various forms of instruction Number of hoursT-L-1 Introduction to software engineering labs. Organisational issues. Preparing student environment. 2

T-L-2 Problem definition and introduction to requirements engineering. 2

T-L-3 Writing user and system specifications 4

T-L-4 User interface wireframing and design, processing design 2

T-L-5 Software analysis and modelling 6

T-L-6 Database design 2

T-L-7 Initial implementation of the prototype of the architecture 2

T-L-8 Completing student projects - documentation and implemntation 8

T-L-9 Project presentation and grading 2

T-W-1 Introduction to software engineering. 2

T-W-2 Gathering customer/user requirements. Writing user and system specifications. 2

T-W-3 Software analysis and modelling. Design patterns. 4

T-W-4 Software designing. Architectural patterns. Data design. User interface wireframing and design.Processing design. Prototyping. 2

T-W-5 Introduction to validation and verification. Software Testing. 4









Software engineering

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Student workload - forms of activity Number of hourspreparing for credit 13A-W-3



M-2 Lab exercises

M-3 Project






study





education

Teachingmethods


methods

Knowledge

C-1 S-3T-W-1T-W-2T-W-3

M-1WM-WI_2-_null_W01Describes main terms, processes and techniques used insoftware engineering.

T-W-4T-W-5T-W-6

Skills

C-1C-2C-3

S-1S-2S-3


M-2M-3

WM-WI_2-_null_U01Can create software project documentation with requirementsspecification, architectural design, and main test cases.



C-1C-3

S-1S-2S-3


M-1M-2M-3

WM-WI_2-_null_K01Ability to communicate with non-technical people




3,0 Student briefly describes main terms, majority of process elements and main techniques used in software engineering.

3,54,04,55,0


3,0 Student can use software tools to create software requirements specification with main elements correctly defined

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3,0 Student can communicate with non-technical people to prepare and present requirements specification and selectedelements of software design

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Required reading1. Bruegge B., Dutoit A.H., Object-Oriented Software Engineering Using UML, Patterns and Java, Prentice Hall, 2009, 3rd edition2. Larman C., Applying UML and Patterns: An Introduction to Object-Oriented Analysis and Design and Iterative Development, PrenticeHall, 2004, 3rd Edition

Supplementary reading1. Freeman E., Bates B., Sierra K., Robson E., Head First Design Patterns, O'Reilly Media, 2004

2. Wiegers K., Beatty J., Software Requirements, Microsoft Press, 2013, 3rd Edition

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Field of study

Mode of study


ECTS


Code

Electives

Language

Elective group


stationary

WI-2-SFP

6,0

credits english

ECTS (forms) 6,0

Level second cycle

Area(s) of study


Module

Course unit Spring Framework Programming






Wierciński Tomasz ([email protected])Leading teacher

Other teachers

PrerequisitesW-1 Java SE programming.

W-2 XML basics.

W-3 SQL and database basics.

W-4 Web application development basics.

Module/course unit objectivesC-1 Familiarization with Java EE language and Spring Framework programming.

C-2 Knows how to develop advanced client-server applications in Java language and Spring.

C-3 Understands the need for further development of professional skills in the field of Java EE.

Course content divided into various forms of instruction Number of hoursT-L-1 Spring IoC container structure. 2

T-L-2 Writing, configuring and injecting beans. 3

T-L-3 Aspect-oriented programming. 4

T-L-4 Using JDBC with Spring. 2

T-L-5 Integrating ORM with Spring. 3

T-L-6 Managing transactions. 2

T-L-7 Building web application with Spring MVC. 3

T-L-8 Securing applications with Spring Security. 3

T-L-9 Working with remote services. 3

T-L-10 Working with Spring JMS. 3

T-L-11 Managing Spring components with JMX. 2

T-W-1 Spring IoC container structure. 1

T-W-2 Writing, configuring and injecting beans. 1

T-W-3 Aspect-oriented programming. 2

T-W-4 Using JDBC with Spring. 2

T-W-5 Integrating ORM with Spring. 1

T-W-6 Managing transactions. 1

T-W-7 Building web application with Spring MVC. 2

T-W-8 Securing applications with Spring Security. 2

T-W-9 Working with remote services. 1

T-W-10 Working with Spring JMS. 1

Spring Framework Programming

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Course content divided into various forms of instruction Number of hoursT-W-11 Managing Spring components with JMX. 1

Student workload - forms of activity Number of hoursParticipation in class 30A-L-1

Preparing to perform exercises 30A-L-2

Self-study of the literature 30A-L-3


Participation in class 15A-W-1

Self-study of the literature 15A-W-2

Exam preparation 15A-W-3

Participation in consultations 13A-W-4

Participation in exam 2A-W-5


M-2 Multimedia presentation

M-3 Laboratory

Evaluation methods (F - progressive, P - final)S-1 Project workF

S-2 Written examP



study





education

Teachingmethods


methods

Knowledge

C-2 S-2


M-1M-2

WM_2-_null_W01Knows how to develop advanced client-server applications inJava language and Spring.

T-W-7T-W-8T-W-9T-W-10T-W-11

Skills

C-1 S-1


M-3WM_2-_null_U01Familiarization with Java EE language and Spring Frameworkprogramming.

T-L-7T-L-8T-L-9T-L-10T-L-11


C-3 S-1

T-L-1T-L-2T-L-3T-L-4T-L-5T-L-6T-L-7T-L-8T-L-9T-L-10T-L-11

M-1M-2M-3

WM_2-_null_K01Understands the need for further development of professionalskills in the field of Java EE.

T-W-1T-W-2T-W-3T-W-4T-W-5T-W-6T-W-7T-W-8T-W-9T-W-10T-W-11



3,0 The student knows the structure and operation of Spring Framework.

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3,0 The student is able to implement a source code in Java and Spring Framework according to the knowledge they gained in theclass.

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Other social / personal competencesWM_2-_null_K01 2,0

3,0 The student understands the need to learn and use Spring Frmework.

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Required reading1. Craig Walls, Spring in Action, Third Edition, Manning Publications Co., 2010, 32. Spring Framework Reference Documentation, 2015, http://docs.spring.io/spring/docs/current/spring-framework-reference/pdf/spring-framework-reference.pdf

THIRD DEGREE (DOCTOR)

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Administracja Centralna UczelniField of study

Mode of study


ECTS


Code

Electives

Language

Elective group


stationary

WI-3-BCI

4,0

credits english

ECTS (formy) 4,0

Level third cycle

Area(s) of study


Module





laboratory course 45 3,0 0,75 creditsL

lecture 15 1,0 0,25 creditsW











T-L-5 Exam. 2






T-W-6 Exam. 2






M-2 Discussion.





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Administracja Centralna Uczelni



studyTeaching methodsCourse objectives Course content Evaluation methods

Knowledge

C-1 S-2

T-L-5T-W-1T-W-2 M-1

M-2

WM-WI_3-_null_W01After the lectures the student will be able to: define a BCI, describethe main problems with EEG data, describe the EEG device, descibedifferent BCI paradigms, choose the processing methods suitable fordifferent paradigms and different EEG data.

T-W-3T-W-4T-W-5

Skills

C-2 S-1T-L-1T-L-2T-L-3

M-3WM-WI_3-_null_U01The student will be able to create the project of a BCI suitable for agiven task.

T-L-4T-L-5





3,54,04,55,0



3,54,04,55,0



Update date: 18-03-2019

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Mode of study


ECTS


Code

Electives

Language

Elective group


stationary

WI-3-CVI

4,0

credits english

ECTS (formy) 4,0

Level third cycle

Area(s) of study


Module

Course unit Computer Vision and Fast Object Detection







Other teachers

PrerequisitesW-1 Algorithmics. Mathematics. Good skills in programming.

Module/course unit objectivesC-1 To familiarize students with techniques and algorithms related to detection of objects in digital images.


T-L-1 Student's own implementation of face detector (or human detector) - a project per whole semester(programming languange of student's choice) 15

T-W-1 Computational complexity of detection procedures based on a sliding window. Extraction of imagefeatures using integral images - constant-time cost per feature. Haar wavelets and Haar-like features. 6

T-W-2 HoG (Histogram of Gradients) descriptor. Augmenting HoG with integral images. Parameterization offeature space. 2

T-W-3Boosting as a learning meta-algorithm suitable for large-scale data and computer vision tasks.Properties of AdaBoost and RealBoost algorithms. Accuracy measures of detectors (sensitivity, FAR,ROC curves, AUC, F1). Cascades of classifiers.

7

Student workload - forms of activity Number of hoursAttendance at labs. 15A-L-1

Self-work (at home) on homework assignment. 75A-L-2

Attendance at lectures. 15A-W-1

Preparation to exam. 15A-W-2

Teaching methods / toolsM-1 Lectures in form of a presentation with mathematical derivations of needed algorithms.


S-1 Lectures - single-choice test.Laboratories - homework assiignment / project (face or body detector).F




KnowledgeWM-WI_3-_null_W01Students will be familiar with techniques and algorithms related todetection of objects in digital images.

SkillsWM-WI_3-_null_U01Hands-on experience in implementation of object detector.


Computer Vision and Fast Object Detection

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Administracja Centralna UczelniOutcomes Grade Evaluation criterion


3,03,54,04,55,0


3,03,54,04,55,0


Required reading1. B. Cyganek, Object Detection and Recognition in Digital Images: Theory and Practice, Wiley, 20132. P. Klęsk, Techniques of fast detection, [pdf presentation by lecturer], 2014,http://wikizmsi.zut.edu.pl/uploads/4/4a/Szybka_detekcja.pdf


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Mode of study


ECTS


Code

Electives

Language

Elective group


stationary

WI-3-EEG

4,0

credits english

ECTS (formy) 4,0

Level third cycle

Area(s) of study


Module

Course unit Brain signal analysis in Matlab environment

















T-L-8 Exam. 2





T-W-5 Exam. 2






M-2 Discussion.






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Administracja Centralna Uczelni




Knowledge

C-1 S-2

T-L-7T-W-1 M-1

M-2

WM-WI_3-_null_W01After the lectures the student will be able to: define a BCI, describethe main problems with EEG data, describe the EEG device, descibedifferent BCI paradigms, choose the processing methods suitable fordifferent paradigms and different EEG data.

T-W-2T-W-3

Skills

C-1 S-1T-L-1T-L-2T-L-3T-L-4

M-3WM-WI_3-_null_U01The student will be able to create the project of a BCI suitable for agiven task.

T-L-5T-L-6T-L-7





3,54,04,55,0



3,54,04,55,0





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Mode of study


ECTS


Code

Electives

Language

Elective group


stationary

WI-3-LAT

2,0

credits english

ECTS (formy) 2,0

Level third cycle

Area(s) of study


Module

Course unit LaTeX - document preparation system for engineers







Other teachers




T-L-1


15

T-W-1


15






Exam 2A-W-3




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Administracja Centralna UczelniTeaching methods / toolsM-1 Lecture with presentation







Knowledge

C-1 S-1T-W-1

M-1WM_3-_null_W01Student has knowledge about typesetting engineering documentswith LaTeX system

Skills

C-1 S-2T-L-1

M-2WM_3-_null_U01Student has practical skills in typesetting of engineering documentswith LaTeX system





3,54,04,55,0



3,54,04,55,0





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Mode of study


ECTS


Code

Electives

Language

Elective group


stationary

WI-3-MBV

3,0

credits english

ECTS (formy) 3,0

Level third cycle

Area(s) of study


Module








Prerequisites









homework 30A-W-2



exam 3A-W-5







KnowledgeWM-WI_3-_??_W01Student understands communication principles


Other social / personal competencesWM-WI_3-_??_K01Student values the importance of communication in business.


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Administracja Centralna UczelniOutcomes Grade Evaluation criterion



3,54,04,55,0



3,54,04,55,0



3,54,04,55,0
