# ECE 302: Probabilistic Methods for Electrical and Computer Engineering

Professor Stanley H. Chan, Purdue University, Spring Semester 2016-2017

## Announcement

12/16/2016 Welcome to ECE 302!

## Course Information

Lecture: MWF 12:30-1:20pm
Room: MSEE B012

Discussion Section: Thursday 3-4pm
Room: EE 117

Instructor: Professor Stanley H. Chan
Room: MSEE 338
Email: stanchan AT purdue DOT edu
Office Hour: Monday 4-5pm. By email appointment.

Teaching Assistant: Mr. I-Fan Lin
Email: lini AT purdue DOT edu
Office Hour: Tu 10-12, Fri 10:30-12, EE 234

## Course Notes

Note that all course materials are copyrighted. Without permission of Prof Chan, students are not allowed to redistribute the materials, including note, homework, projects and exams.

## Lecture

• Jan-09 Lecture 00: Introduction

• Jan-11 Lecture 01: Series, Set theory (Ch. 1.1 and Ch. 1.2)

• Jan-13 Lecture 02: Combinatorics, Matrix, Probability Model (Ch 1.3 and Ch. 1.4)

• Jan-16 No Class: MLK Day

• Jan-18 Lecture 04: Probability Axioms, Conditional Probability (Ch. 2.1 and 2.2)

• Jan-20 Lecture 05: Conditional Probability, Bayes Rule (Ch. 2.2, Ch. 2.4)

• Jan-23 Lecture 06: Law of Total Probability, Independence (Ch. 2.3 - Ch. 2.4)

• Jan-25 Lecture 07: Discrete Random Variables, PMF (Ch. 3.1 - Ch. 3.2)

• Jan-27 Lecture 08: PMF, CDF, Expectation (Ch. 3.2)

• Jan-30 Lecture 09: Lecture by Professor Mary Comer: Expectation, Moment (Ch. 3.3 - Ch. 3.4)

• Feb-01 Lecture 10: Lecture by Professor Mary Comer: Moment, Variance (Ch. 3.5)

• Feb-03 Lecture 11: Expectation, Moment, Variance, Bernoulli RV (Ch. 3.3 - Ch. 3.5)

• Feb-06 Lecture 12: Bernoulli RV, Binomial RV, Geometric RV (Ch. 3.5) MATLAB demo 1

• Feb-08 Lecture 13: Poisson RV, Poisson-Binomial Approximation (Ch. 3.5) MATLAB demo 1, MATLAB demo 2

• Feb-10 Lecture 14: PDF, Expectation, Moment, Variance (Ch. 4.1, Ch. 4.3)

• Feb-13 Lecture 15: Mode, Mediam, CDF (Ch. 4.2 - Ch. 4.3)

• Feb-15 Lecture 16: Uniform Distribution, Exponential Distribution, Gaussian Distribution (Ch. 4.5)

• Feb-17 Lecture 17: Gaussian Distribution (Ch. 4.5)

• Feb-20 Mid Term 1: Chapter 1-3. Lecture 1-13. HW 1 - 5. 8pm-9pm, MATH 175 (Proctor by Prof. Mimi Boutin)

• Feb-22 Lecture 18: Function of Random Variables (Ch. 4.6)

• Feb-24 Lecture 19: Joint PMF, PDF, Conditional PMF, PDF (Ch. 5.1, Ch. 5.3)

• Feb-27 Lecture 20: Joint CDF, Joint Expectation (Ch. 5.2, Ch. 5.4)

• Mar-01 Lecture 21: Covariance, Correlation (Ch. 5.4)

• Mar-03 Lecture 22: Two-dimensional Gaussian (Ch. 5.6)

• Mar-06 Lecture 23: Conditional Expectation (Ch. 5.5)

• Mar-08 Lecture 24: Lecture by Professor Amy Reibman: Conditional Expectation (Ch. 5.5)

• Mar-10 Lecture 25: Function of Two Random Variables (Ch. 6.1)

• Mar-13 No Class: Spring Break

• Mar-15 No Class: Spring Break

• Mar-17 No Class: Spring Break

• Mar-20 Lecture 26: Moment Generating Function (Ch. 6.2)

• Mar-22 Lecture 28: Characteristic Function (Ch. 6.3)

• Mar-24 Lecture 29: Characterisitic Function (Ch. 6.3)

• Mar-27 Mid Term 2: Chapter 4.1 - 6.3. Lecture 14-29. HW 6 - 9. 8pm-9pm, MATH 175 (Proctor by Prof Mimi Boutin)

• Mar-31 Lecture 30: Linear regression (Ch. 7.1)

• Apr-03 Lecture 31: Maximum Likelihood Estimation (Ch. 7.2)

• Apr-05 Lecture 32: Maximum a Posteriori Estimation (Ch. 7.3)

• Apr-07 Lecture 33: Lecture by Professor Amy Reibman

• Apr-10 Lecture 34: Random process, mean function, auto-correlation function (Ch. 8.1 - 8.2)

• Apr-12 Lecture 35: Cross-correlation function, wide sense stationarity, power spectral density (Ch. 8.3 - 8.4) MATLAB demo 1, MATLAB demo 2

• Apr-14 Lecture 36: Wide sense stationary processes, Power spectral density (Ch. 8.2 - 8.4)

• Apr-17 Lecture 37: Linear predictive code (Project 3)

• Apr-19 Lecture 38: Random process through LTI systems (Ch. 9.1 - 9.2)

• Apr-21 Lecture 39: Cross power spectral density (Ch. 9.3)

• Apr-24 Lecture 40: Optimal linear filters (Ch. 9.4)

• Apr-26 Lecture 41: Guest Talk by Dr. Suhas Sreehari

• Apr-38 Lecture 42: Review