CMSC5728: Decision Analysis and Game Theory
Student/Faculty Expectations on Teaching and Learning
Prof. John C.S. Lui
This is a graduate level course which covers theory on decision science.
There are several main topics I plan to cover, they are:
(a) Multi-armed bandit theory;
(b) Game theory;
(c) Reinforcement learning theory.
I like to emphasize that course
is mathematical and algorithmic in nature.
I will introduce a lot of concepts, show the mathematical proves,
and present the physical meanings and applications.
Students are expected to follow and understand my lecture,
and also do a lot of readings and do some programming (via Python).
are expected to attend the lecutre,
read the leture notes and understand them,
spend time to read resources on the Internet,
do the homework,
do the programming assignments,..etc,
so to keep pace with this course.
- Written Homework and/or Programming Assignment: 50%
- Exam: 50%
Students need to get at least 30% in the final exam to pass, independent
of their performance in programming exercises.
IMPORTANT REMINDERS !!!!!!
- Final Examination will be on December XXX, 2020. X:XX pm till Y:YY pm.
Venue will be XXX YYY.
- No late homework, programming assignments or projects will be accepted;
Outline for the course:
(Note: I usually prepare more materials
than we can cover in a semester. I will leave those materials I can't
cover to students as a self-learning tool.)
- Introduction to topics on decision science
- Introduction to Game theory
- Two-player game zero-sum games
- Dominance stratey
- Saddle point
- Mixed strategy
- Minimax theorem
- Two-player game non-zero sum games
- Concept of equilibrium (Nash Equilibrium)
- Cournot Model of Duopoly
- Dynamic Games
- Kuhn's Theorem
- Concept of Subgame
- Subgame Perfect Nash Equilibrium
- Games with continuous strategy space
- Stackelberg Games
- Introduciton to Coalition and Cooperative Games
- Mechanism Design
- Stochastic multi-armed bandit (MAB)
- UCB algorithms and regret bound
- Thompson Sampling and its application to MAB
- Adersarial Bandits
- Linear Bandits
- Contextual Bandit
- MAB application: Dynamic Pricing, networking, crowdsourcing and multi-path protocols
- Markov Decision Process
(Lecture Notes are available at CUHK Blackboard (https://blackboard.cuhk.edu.hk/))
Please refer to the CUHK Blackboard
Written homework and programming assignment
Please go to the "Blackboard" to access the specification.