York University


EECS 3602 Systems and Random Processes in Discrete Time

(Fall 2020)



 
The Lassonde School of Engineering
Department of Electrical Engineering & Computer Science





Content starts here

Content starts here

Instructor: Prof. Gene Cheung

Lecures: TF 13:00-14:30

Location: Virtual! (See links in eClass)

Labs: R 15:00-18:00, R 13:00-16;00

Location: Virtual! (See links in eClass)

Office Hours: TR 11:00-12:00

Announcement

  • 08/23/2020: Course homepage online.

Course Summary

This course covers probability theory and stochastic processes. Topics covered include counting methods, discrete / continuous random variables, joint distributions, limit theorems, statistical inference, Poisson process, Gaussian process, Markov chains. The prerequisite is SC/MATH 2930; EECS2030 / EECS1030; EECS2602.

Required Textbook

  • H. Pishro-Nik, Introduction to Probability, Statistics, and Random Processes, Kappa Research, LLC, 2014. (also available online HERE)

Supplementary Material

  • R. G. Gallager, Stochastic Processes: Theory for Applications, Cambridge University Press, 2013.
  • S. Boyd, L. Vandenberghe, Introduction to Applied Linear Algebra: Vectors, Matrices, and Least Squares,  Cambridge University Press, 2018.

Evaluation

  • Bi-weekly assignments (30%)
  • Lab assignments (20%)
  • Midterm (25%)
  • Final (25%)

Course Outline (subject to change)

  • Week 1: Course Overview
  • Week 2: Counting Methods
  • Week 3: Discrete Random Variables
  • Week 4: Continuous Random Variables
  • Week 5: Joint Distribution / Multiple Random Variables
  • Week 6 : Statistical Inference 1: Classical Methods
  • Week 7: Statistical Inference 2: Bayesian Inference
  • Week 8: Poisson processes
  • Week 9: Gaussian Random Vectors and Processes
  • Week 10: Finite-state Markov Chains 1
  • Week 11: Finite-state Markov Chains 2

last modified December 12, 2020

 

 

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