|
Instructor:
Prof.
Gene
Cheung
Lectures:
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
|
