York University


EECS 3602 Systems and Random Processes in Discrete Time

(Fall 2019)



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





Content starts here

Content starts here

Instructor: Prof. Gene Cheung

Lecures: TR 13:00-14:30

Location: HNE 037 / HNE 032

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

Location: BRG 336

Office Hours: TF 11:00-12:00 (LAS 2012)

Announcement

  • 09/02/2019: Check Moodle for uploaded lecture slides.
  • 09/02/2019: 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)

    R. G. Gallager, Stochastic Processes: Theory for Applications, Cambridge University Press, 2013.

Supplementary Material

  • S. Boyd, L. Vandenberghe, Introduction to Applied Linear Algebra: Vectors, Matrices, and Least Squares,  Cambridge University Press, 2018.

Evaluation

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

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
  • Week 11: Renewal processes



last modified December 20, 2019

 

 

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