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Instructor:
Prof.
Gene
Cheung
Lectures:
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
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
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