Subject: Computer Science
Credit units: 3
Offered: Either Term 1 or Term 2
Weekly hours: 3 Lecture hours and 1 Tutorial hours
College: Arts and Science
Department: Computer Science


A survey of essential Artificial Intelligence techniques and underlying theory. Basic search strategies, including uninformed search, heuristic search, and games. Basic knowledge representation and reasoning, including propositional satisfiability and theorem proving, Bayes rule, and Bayesian networks. Basic machine learning, including k-nearest neighbours, decision trees, neural networks, naive Bayes classifier, k-means.

Prerequisite(s): CMPT 260; and CMPT 280; and STAT 245 or equivalent (including EE 216 or ME 251).

Upcoming class offerings

For full details about upcoming courses, refer to the class search tool or, if you are a current student, the registration channel in PAWS.


The syllabus is a public document that provides detail about a class, such as the schedule of activities, learning outcomes, and weighting of assignments and examinations.

Once an instructor has made their syllabus publicly available on USask’s Learning Management System, it will appear below. Please note that the examples provided below do not represent a complete set of current or previous syllabus material. Rather, they are presented solely for the purpose of indicating what may be required for a given class. Unless otherwise specifically stated on the content, the copyright for all materials in each course belongs to the instructor whose name is associated with that course. The syllabus is the intellectual property of instructors or the university.

For more information, visit the Academic Courses Policy , the Syllabus page for instructors , or for students your Academic Advising office.