CMPUT 466 - Machine Learning

★ 3 (fi 6)(EITHER, 3-0-3)

Faculty of Science

Learning is essential for many real-world tasks, including recognition, diagnosis, forecasting and data-mining. This course covers a variety of learning scenarios (supervised, unsupervised and partially supervised), as well as foundational methods for regression, classification, dimensionality reduction and modeling. Techniques such as kernels, optimization and probabilistic graphical models will typically be introduced. It will also provide the formal foundations for understanding when learning is possible and practical. Prerequisites: one of CMPUT 340 or 418; one of STAT 141, 151, 235 or 265 or SCI 151; or consent of the instructor.

Winter Term 2021

Lectures

LECTURE B1 (44286)
2021-01-04 - 2021-04-09
TH 11:00 - 12:20 (TBD)

Primary Instructor: Alona Fyshe

Labs

LAB H01 (44287)
2021-01-04 - 2021-04-09
W 17:00 - 19:50 (TBD)

Fall Term 2021

Lectures

LECTURE A1 (48976)
2021-09-01 - 2021-12-07
TH 12:30 - 13:50 (TBD)

Labs

LAB D01 (48978)
2021-09-01 - 2021-12-07
M 17:00 - 19:50 (TBD)

Winter Term 2022

Lectures

LECTURE B1 (63596)
2022-01-05 - 2022-04-08
TH 11:00 - 12:20 (TBD)

Labs

LAB H01 (63598)
2022-01-05 - 2022-04-08
W 17:00 - 19:50 (TBD)