Fall Term 2026 (1970)
CMPUT 466 - Machine Learning Essentials
3 units (fi 6)(EITHER, 3-0-3)
Learning is essential for many real-world tasks, including recognition, diagnosis, forecasting and data-mining. This course provides a broad overview of topics in machine learning, from foundational methods for regression, classification and dimensionality reduction to more complex modeling with neural networks. It will also provide the formal foundations for understanding when learning is possible and practical. This single course is an alternative to the more in-depth two-course sequence on machine learning with CMPUT 267 and CMPUT 467. Prerequisites: CMPUT 204 or CMPUT 275; any 300-level Computing Science course; MATH 102, 125, 126, or 127; one of MATH 115, 118, 136, 146, or 156; and one of STAT 151, 161, 181, 235, 265, SCI 151, or MATH 181. Credit cannot be obtained in CMPUT 466 if credit has already been obtained for CMPUT 467.
LECTURE A1 (52437)
2026-09-01 - 2026-12-08
TR 12:30 - 13:50
CMPUT 566 - Machine Learning Essentials
3 units (fi 6)(VAR, VARIABLE)
Learning is essential for many real-world tasks, including recognition, diagnosis, forecasting and data-mining. This course provides a broad overview of topics in machine learning, from foundational methods for regression, classification and dimensionality reduction to more complex modeling with neural networks. It will also provide the formal foundations for understanding when learning is possible and practical. Credit cannot be obtained for both CMPUT 466 and 566.
LECTURE A1 (52818)
2026-09-01 - 2026-12-08
TR 12:30 - 13:50
Winter Term 2027 (1980)
CMPUT 463 - Probabilistic Graphical Models
3 units (fi 6)(EITHER, 3-0-3)
Probabilistic graphical models (PGMs; including Bayesian Belief Nets, Markov Random Fields, etc.) now contribute significantly to many areas, including expert systems, computer perception (vision and speech), natural language interpretation, automated decision making, and robotics. This course provides an introduction to this field, describing semantics, inference and learning, as well as practical applications of these systems. Programming assignments will include hands-on experiments with various reasoning systems. Prerequisites: one of CMPUT 204 or CMPUT 275; and one of CMPUT 267 or CMPUT 466.
LECTURE B1 (83589)
2027-01-04 - 2027-04-09
TR 12:30 - 13:50
LAB H01 (83606)
2027-01-04 - 2027-04-09
R 17:00 - 19:50
CMPUT 563 - Probabilistic Graphical Models
3 units (fi 6)(VAR, VARIABLE)
Probabilistic graphical models (PGMs; including Bayesian Belief Nets, Markov Random Fields, etc.) now contribute significantly to many areas, including expert systems, computer perception (vision and speech), natural language interpretation, automated decision making, and robotics. This course provides an introduction to this field, describing semantics, inference and learning, as well as practical applications of these systems. Programming assignments will include hands-on experiments with various reasoning systems. Credit cannot be obtained for both CMPUT 463 and 563.
LECTURE B1 (83596)
2027-01-04 - 2027-04-09
TR 12:30 - 13:50
LAB H01 (83609)
2027-01-04 - 2027-04-09
R 17:00 - 19:50