CMPUT 366 (Winter 2020)

Intelligent Systems


Class Times:
Mondays, Wednesdays, and Fridays 11:00–11:50am
First class:
January 6, 2020
BS M 149
James Wright (
ATH 3-57
Office hours:
Available after class on Mondays and Fridays, and by appointment.
Thursdays 5:00pm to 8:00pm (BS M 149)

Introduction to modern artificial intelligence techniques, with a focus on probabilistic reasoning. Specific topics include uninformed and heuristic search, probabilistic modeling and reasoning, causal inference, deep learning, Bayesian learning, reinforcement learning, and multiagent systems. The course will emphasize the importance of appropriate choices of formal model.


After taking this survey course, you will understand the foundations of modern probabilistic artificial intelligence and how they relate to each other, in preparation for taking more advanced courses. You will understand the strengths and weaknesses of the broad families of representations in each area. You will be able to choose appropriate models in application domains, and be able to encode specific problems in those models effectively.


Grade breakdown
Late assignments

Assignments are to be handed in electronically via eClass by the start of lecture on the due date. Late assignments will have 20% deducted for each day that the assignment is late, up to a maximum of three days late.

Academic conduct

Submitting the work of another person as your own constitutes plagiarism. The department is very strict about plagiarism and other academic misconduct: ALL forms of cheating are referred to the Dean’s office.

The rules for this course allow consultation collaboration. The specific rules are:


Students are responsible only for material that is presented in class. Slides will be made available on eClass on the day of the corresponding lecture.

Optional readings will be provided from the following texts, all of which are available online: