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Instructor:
Prof.
Gene
Cheung
Lectures:
T/R
14:30-16:00
Location:
(See
info in eClass)
Labs:
M
9:00-11:00, R 12:00-14;00
Location:
(See
info in eClass)
Office
Hours:
T
16:00-17:00
Announcement
- 01/01/2026: eClass page is online.
- 06/10/2025: Course homepage is online.
Course
Summary
Probability theory and applications for electrical
engineers. Counting methods, multiple discrete /
continuous random variables, functions of random
variables, statistical inference, Poisson process,
finite-state Markov chain, Markov decision theory
for communications and signal processing
applications.
Prerequisites are SC/MATH 2930, SC/MATH 2015.
Course
Learning Outcomes
- Counting: Count
possible outcomes in a discrete random event,
under ordered / unordered sampling with /
without replacement scenarios.
- Probability:
Model the probabilities of a random event
using functions of one or multiple random
variables, discrete or continuous.
- Inference:
Apply statistical inference principles to
infer model parameters from data.
- Stochastic
Processes: Model time-varying random
events using stochastic models including Poisson and Gaussian
processes and Markov chain.
- Labs:
Demonstrate the use of probability theory
through hands-on group lab exercises or
otherwise for various practical engineering
scenarios, examining, for example, transform
coding in image compression, random signal
processing, and channel coding in digital
communication (e.g., using contemporary
software tools).
- Team:
Review, during aforementioned group exercises,
within the group, the strengths and weaknesses
of the team members and document how the group
leveraged these strengths and addressed the
weaknesses (e.g., coaching, tutoring, group
study sessions).
- Relevance:
Explain the relationship of stochastic or
probabilistic processes to important
contemporary political, social, legal, or
environmental issues and/or values (e.g.,
design of image compression algorithms
vis-a-vis skin colour).
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: Reading Week
- Week 8: Review & Midterm Exam
- Week 9: Statistical Inference 2: Bayesian
Inference
- Week 10: Poisson processes
- Week 11: Discrete-time Markov Chains
- Week 12: Markov decision theory
- Week 13: Final review
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