fundamentals of machine learning bootcamp - 24 nov london 2014
DESCRIPTION
Fundamentals of Machine Learning Bootcamp will take you through the conceptual and applied foundations of the subject. Topics covered will include Machine Learning theory, types of learning, techniques, models and methods. Labs are developed to practically learn how to use the R programming language and packages for applying the main concepts and techniques of Machine Learning. For corporate bookings or to organize on-site training email [email protected] call now +44 (0)20 3239 3141 www.persontyle.comTRANSCRIPT
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FUNDAMENTALS OF
MACHINE LEARNING
BOOTCAMP
HANDS-ON INTRODUCTION TO MACHINE LEARNING MODELS, METHODS AND ALGORITHMS
www.persontyle.com© 2014 Persontyle Ltd. All rights reserved.
“THE FIELD OF MACHINE LEARNING IS
CONCERNED WITH THE QUESTION OF HOW TO
CONSTRUCT COMPUTER PROGRAMS THAT
AUTOMATICALLY IMPROVE WITH EXPERIENCE.”- TOM MITCHELL
MACHINE
LEARNING
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Data generated through our activities captures plethora of information
about our identity, likes and dislikes etc. This information has tremendous
value in every aspect of human life. Programming computers to unravel this
hidden information is what Machine Learning is all about. It is the art and
science of scientifically deriving insights, patterns and predictions from data.
Though it has been an area of active research for over 50 years, Machine
Learning is currently undergoing a renaissance driven by Moore's law and
the rise of big data. Large private and public investment in the area has
given us self-driving cars, practical speech recognition, effective web search,
and a vastly improved understanding of the human genome. Computer
based Machine Learning algorithms now outperform humans on tasks such
as handwritten digit recognition, traffic sign recognition, and even on some
complex reasoning tasks as demonstrated by IBM's Watson winning
Jeopardy.
Machine Learning models and programs automatically make decisions from data inorder to achieve some goal or requirement. Machine learning models matter to theworld. Because they are;
# EFFICIENTMachine Learning models predict and detect partners faster than any other manualprogram or method.
# EFFECTIVEMachine Learning models can do better job than humans when analysing andpredicting large scale and streaming data sets (big data).
# SCALEMachine Learning models can provide solutions to large data problems thattraditional systems can not solve.
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Machine perception
Computer vision,
including object recognition
Natural language processing
Pattern recognition
Search engines
Medical diagnosis
Bioinformatics
Brain-machine interfaces
Detecting credit card fraud
Stock market analysis
Classifying DNA sequences
Sentiment analysis
Affective computing
Information retrieval
Recommender systems
Examples in the real world include handwritten recognition,
weather prediction, fraud detection, search, facial recognition, and
so forth are all examples of machine learning in the wild.
Applications for Machine Learning include:
“Over the past two decades Machine Learning has become one of the
mainstays of information technology and with that, a rather central, albeit
usually hidden, part of our life. With the ever increasing amounts of data
becoming available there is good reason to believe that smart data analysis
will become even more pervasive as a necessary ingredient for
technological progress.”
DR. ALEXANDER J. SMOLA, PROFESSOR, CARNEGIE MELLON UNIVERSITY
www.persontyle.com© 2014 Persontyle Ltd. All rights reserved.
Machine Learning enables computational systems to adaptively improve
their performance with experience accumulated from the observed data.
Though it has been an area of active research for over 50 years, Machine
Learning is currently undergoing a renaissance driven by Moore's law and
the rise of big data. Large private and public investment in the area has
given us self driving cars, practical speech recognition, effective web
search, and a vastly improved understanding of the human genome.
Computer based machine learning algorithms now outperform humans on
tasks such as handwritten digit recognition, traffic sign recognition, and
even on some complex reasoning tasks as demonstrated by IBM's Watson
winning Jeopardy.
Fundamentals of Machine Learning Bootcamp will take you through the
conceptual and applied foundations of the subject. Topics covered will
include Machine Learning theory, types of learning, techniques, models
and methods. Labs are developed to practically learn how to use the R
programming language and packages for applying the main concepts and
techniques of Machine Learning.
Over the course of five days, over two dozen techniques will be examined,
implemented through supervised exercises and tutorials, and compared.
You will learn the relative advantages and disadvantages of different types
of techniques in different contexts. You will see how some models are
entirely data driven, while others can be used to encode defeasible expert
knowledge. You will learn methods for validating selected models and
techniques and for choosing among alternative methods.
FUNDAMENTALS OF
MACHINE LEARNING
BOOTCAMP
WHAT WILL YOU LEARN?
In this bootcamp you will learn, among other things:
+ What Machine Learning entails and why it is
important
+ The different types of Learning, especially Supervised
Learning
+ Be able to use R to apply a number of the most
common and powerful statistical machine learning
techniques.
+ Know how to implement such techniques in principle
and therefore be able to apply their knowledge within
paradigms outside R.
+ Be able to appreciate the trade-offs involved in
choosing particular techniques for particular
problems.
+ Be able to utilize rigorous methods of model
selection.
+ Understand the mathematical ideas behind, and
relationships between, the various methods.
+ Have a greater confidence in their knowledge and
standing as a data scientist.
+ How to use these algorithms in a variety of
benchmark datasets
+ How to fine-tune these algorithms for better
performance
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R logo is trademark of the R Foundation, from http://www.r-project.org
PREREQUISITES
Knowledge of R programming language and familiarity with linear algebra.
Basic familiarity with statistics and probability theory is recommended.
SCHEDULE AND LEARNING OBJECTIVES
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Time Topic/Activity
09:00-09:30 Introduction
09:30-11:00 1. R Refresher
11:00-13:00 2. Linear and Quadratic Regression
After this module, you will:• Understand what regression is.• Understand what linearity is.• Understand the idea behind basis projection.• Be able to perform linear, quadratic and polynomial regression.• Be able to identify datasets that are suitable for linear and quadratic
regression.• Understand the idea of free parameters.
13:00-13:30 Lunch
13:30-15:00 2. Principle Component Analysis
After this module, you will:
• Understand how PCA functions.
• Understand how PCA can be used for feature selection and information
compression.
• Be able to perform PCA analysis and regression.
• Understand and be able to perform scaling and centring of data.
15:00 -15:15 Coffee Break
15:15-17:15 3. Feature Selection and Shrinkage
After this module, you will:
• Understand the idea of feature shrinkage
• Be able to use subset selection as a means of feature selection
• Be able to use Ridge Regression and the Lasso as means of feature
shrinkage.
• Understand what degrees of freedom are.
• Understand what the variance/bias trade-off is.
• Have a basic understanding of how both relate to the question of model
selection.
17:15-18:00 4. Error Estimation
After this module, you will:
• Know what residuals are
• Be able to model regression error using a normal distribution.
DAY 1 DAY 2 DAY 3 DAY 4 DAY 5
SCHEDULE AND LEARNING OBJECTIVES
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Time Topic/Activity
9:00-11:00 5. Real-Discrete Classification: LDA, QDA and Logistic Regression
After this module, you will:• Understand what classification tasks are, and the difference between real-
discrete and discrete-discrete classification.• Be able to apply LDA, QDA and Logistic Regression.
11:00-11:15 Coffee Break
11:15-13:00 6. Perceptron Classification
After this module you will:• Understand how to use the perceptron classifier in separable and inseparable
cases.• Understand the idea of linearly separable and inseparable data.• Understand the idea of iterative algorithms and termination conditions.
13:00-13:30 Lunch
13:30-15:30 6. Discrete-Discrete Classification & An Introduction to Bayesian Methods
After this module, you will:• Be able to apply conditional multinomial and noisy-or models to discrete-
discrete classification tasks.• Understand the idea behind Bayesian Methods in statistics• Be able to work with Dirichlet priors, and understand the idea of count and
pseudo-count parameters.
15:30-15:45 Coffee Break
15:45-17:45 7. K-Means and Cluster Analysis
After this module, you will:• Understand and be able to compute the distance between data points.• Understand unsupervised learning and cluster analysis.• Be able to apply the K-Means and K-Mediod algorithms for cluster analysis.
DAY 1 DAY 2 DAY 3 DAY 4 DAY 5
SCHEDULE AND LEARNING OBJECTIVES
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DAY 1 DAY 2 DAY 3 DAY 4 DAY 5
Time Topic/Activity
9:00-11:00 8. K Nearest Neighbours
After this module, you will:• Understand what is meant by local methods, their weakness regarding memory
use, and the situations in which they are suitable• Be able to apply the K-Nearest-Neighbours and Adaptive K-Nearest-Neighbours
techniques
11:00-11:15 Coffee Break
11:15-13:00 9. Local Regression
After this module, you will:• Be able to perform local regression.
13:00-13:30 Lunch
13:30-15:30 10. Kernel Density Estimation
After this module, you will:• Understand what a kernel is.• Be able to identify common kernels.• Understand what bandwidth is and why it is important.• Be able to perform kernel density estimation.• Understand what thinning is and be able to perform thinned kernel density
estimation using K-Means or K-Mediods.• Be able to identify cases where kernel density estimation is suitable.
15:30-15:45 Coffee Break
15:45-18:00 11. Regression/Classification Trees and Boosting
After this module, you will:• Understand and be able to implement regression/classification trees.• Understand what boosting is.• Be able to implement the adaboost algorithm.
SCHEDULE AND LEARNING OBJECTIVES
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DAY 1 DAY 2 DAY 3 DAY 4 DAY 5
Time Topic/Activity
9:00-11:30 12- Splines
After this module, you will:• Understand what truncated exponential splines are and how we can use
bases projection to calculate them.• Understand the border issues associated with regression splines and how
natural splines assist in dealing with these. • Understand what B-Splines are and how they are used.• Be able to use truncated exponential regression and natural splines, as well
as B-Splines. • Be able to work with tensor products of such splines
11:30-13:00 13. MARS
After this module, you will:• Be able to use the MARS procedure for working with splines.• Be able to identify cases where such additive methods are appropriate.• Understand the idea of effective degrees of freedom.
13:00-13:30 Lunch
13:30-14:15 Azure Machine Learning Studio Overview – 1
14:15-16:30 14. Smoothing / Thin Plate Splines
After this module, you will:• Understand what smoothing splines are, their optimality guarantees and
their complexity issues.• Understand the connection between penalizing the second derivative of
smoothing splines and performing Ridge Regression on a transform of the dataset.
16:30-18:30 15. Radial Basis Networks
After this module, you will:• Understand what radial basis functions and networks are, how they make
use of kernels to project our data to new bases and the connection with ridge regression to smooth the resulting models.
• Be able to use Radial Basis Networks to model data.• Be able to use appropriate thinning strategies to avoid the complexity
issues identified.
SCHEDULE AND LEARNING OBJECTIVES
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DAY 1 DAY 2 DAY 3 DAY 4 DAY 5
Time Topic/Activity
09:15-10:15 16. Support Vector Classifiers
After this module, you will:• Know what support vectors, optimal hyperplanes and support vector
classifiers are.
10:15-12:15 17. Support Vector Machines
After this module, you will:• Understand how SVMs work, the reasons for their success, and the links
between them and simpler statistical models from earlier modules.• Be able to apply support vector machines to appropriate cases.
12:15-13:00 Azure Machine Learning Studio Overview – 2
13:00-13:30 Lunch Break
13:30-16:45 18. Neural Networks
After this module, you will:• Understand how Neural Networks work, the reasons for their success, and
the links between them and simpler statistical models from earlier modules.• Be able to train Neural Networks for classification and regression tasks using
the back-propagation algorithm with weight decay.• Be able to apply Neural Networks to appropriate cases.
16:45-18:15 19. Model Selection
After this module, you will:• Be able to apply validation and information criteria model selection methods
to real life problems.• Understand the advantages and disadvantages of the different methods.• Understand the relationship between model fitness and complexity measures
such as effective degrees of freedom.
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Persontyle trainers are passionate about meeting each participants
learning needs. They have been chosen both for their extensive practical
Data Science and Machine Learning experience and for their ability to
educate and interact with natural empathy. All of our trainers have worked
on a variety of data science and Machine Learning projects. They share
their academic knowledge and real-world experience and each individual
adds their own unique perspective to the course. Our trainers present in a
style that is informal, entertaining and highly interactive.
Guest Speakers
Business leaders, Machine Learning practitioners, and academic
researchers covering use cases, case studies and sharing practical
experience of applying Data Science and Machine Learning in their
organizations.
COURSE INSTRUCTORS
“A BREAKTHROUGH IN MACHINE LEARNING
WOULD BE WORTH TEN MICROSOFTS” BILL GATES, CHAIRMAN, MICROSOFT
WHO SHOULD ATTEND
Anyone interested in learning and applying machine learning methods and
R to solve real-world data problems. Ideal for people interested in
pursuing career in data science.
This hands-on workshop is aimed at business and technology
professionals, Developer, Architect, Manager, Data Analyst, BI
Developer/Architect, QA, Performance Engineers, Sales, Pre Sales and
Marketing, Project Manager, Public Services, Teaching Staff and all
those who already have some basic competence in statistics but wish to
begin using R for machine learning for the first time.
For corporate bookings or to organize on-site training email
[email protected] or call now +44 (0)20 3239 3141
Register Now
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