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

A survey of Machine Learning techniques, their underlying theory, and their application to realistic data. Machine learning techniques may include Neural Networks, Support Vector Machines, Bayesian networks, Hidden Markov Models, Particle Filtering; Expectation-Maximization; Sampling; Evaluation methodologies; Over-fitting and Regularization. Software tools will be introduced for practical application.

Prerequisite(s): CMPT 317, STAT 245, and MATH 164 .

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

• Class search
• Registration channel

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.

• Bookstore Textbook Search
• Library Reserves Search
• USask Tutoring Network