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

Syllabus: Download (PDF)

Class Agreement: Download (PDF)

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

Useful Materials

Homework

Homework Videos

Projects

Exams

Old Exams