ee 320: applied probability and...
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EE 320: Applied Probability and Statistics
Lecture Schedule See Time Table Course Type,
Semester Fundamental Engineering, Fifth
Credit Hours Three Pre-requisite Linear Algebra &
Signals and Systems
Instructor Haroon A Babri and Asim Loan Contact [email protected]
Office Ground Floor, EE Department Office Hours Schedule is posted outside the office
Teaching Assistant None Lab Schedule N/A
Course Description
The course covers: axiomatic foundations of probability theory, random variables, distributions, and densities, functions of one and several random variables, moment generating functions, random vectors, sequences, convergence, random process, stationarity and second moment theory.
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es CLOs Description Taxonomy
Domain & Level PLOs, Levels
CLO1 Identify elements of probability theory and apply these to various problems in engineering.
Cognitive 1
PLO1 Low
CLO2 Determine densities/distributions and expectations of discrete and continuous, single and multiple, random variables.
Cognitive, 3
PLO2 Medium
CLO3 Illustrate stationarity and describe second moment theory for random processes.
Cognitive 2
PLO1 Low
CLO4 Use Matlab to generate and transform different random variables.
Cognitive 3
PLO5 Medium
Textbooks
REQUIRED: Probability, Random Variables and Stochastic Processes by Athanasios Papoulis and S. U. Pillai, 4th Edition, McGraw Hill, 2002 OPTIONAL: Introduction to Probability by Dimitri P. Bertsekas and John N. Tsitsiklis, 2nd Edition, Athena Scientific, 2008 Probability, Statistics and Random Processes for Electrical Engineering by Alberto Leon Garcia, 3rd Edition, Prentice Hall, 2008
Grading Policy vis-à-vis CLO Mapping
Quizzes ( 6) + Assignments: 30% CLO1 & CLO2 Midterm: 30% CLO1 & CLO2 Final: 40% CLO1 to CLO4
Lecture Plan
Weeks Topics Readings & CLOs
2*
Basic Concepts Axiomatic Probability Theory Conditional Probability Total Probability Theorem and Bayes’ Theorem Independent Events
Chapter 2 CLO1
1* Repeated Trials
Combined Experiments Bernoulli Trials
Chapter 3 CLO1
2* Random Variables
Distribution and Density Functions Conditional density functions
Chapter 4 CLO1, CLO2 &
CLO4
3*
Functions of One Random Variable, Y = g(X) Distribution and Density Functions Moments Inequalities Conditional density functions
Chapter 5 CLO2 & CLO4
Mid-semester Examination
3*
Functions of Two Random Variables, Z = g(X, Y) Distribution and Density Functions Moments Conditional density functions Conditional expectations
Chapter 6 CLO2 & CLO4
2.5*
Sequence of Random Variables Sum, Product Random Vector Correlation and Covariance Matrices Mean square estimation Convergence and Limit Theorems
Chapter 7 CLO2 & CLO4
1.5*
Stochastic Process Introduction Strict Sense Stationary Wide Sense Stationary Second Moment Theory
Chapter 9 CLO3
1* Revision Final Examination
* - Tentative