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. Credit cannot be obtained for both CMPUT 367 and CMPUT 466. Prerequisites: CMPUT 204 or 275; MATH 125; CMPUT 267 or MATH 214; or consent of the instructor.

No syllabi

Fall Term 2023

Lectures

Section Capacity Class times Instructor(s)
LECTURE A1
(83592)
85
2023-09-05 - 2023-12-08 (TR)
12:30 - 13:50
CAB 265

Final Exam:
2023-12-12
09:00 - 12:00
CCIS L1-140
Primary Instructor: Lili Mou

Labs

Section Capacity Class times Instructor(s)
LAB D01
(83593)
85
2023-09-05 - 2023-12-08 (M)
17:00 - 19:50
CCIS 1-160

Winter Term 2024

Lectures

Section Capacity Class times Instructor(s)
LECTURE B1
(10919)
135
2024-01-08 - 2024-04-12 (TR)
11:00 - 12:20
CCIS 1-140
Primary Instructor: Bailey Kacsmar

Labs

Section Capacity Class times Instructor(s)
LAB H01
(10920)
135
2024-01-08 - 2024-04-12 (W)
17:00 - 19:50
CCIS L1-160