Lili Mou, PhD

Assistant Professor, Faculty of Science - Computing Science
Directory

Winter Term 2023 (1820)

CMPUT 466 - Machine Learning

★ 3 (fi 6)(EITHER, 3-0-3)

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: CMPUT 204 or 275; MATH 125; CMPUT 267 or MATH 214; or consent of the instructor.

LECTURE B1 (41241)

2023-01-05 - 2023-04-12
TH 11:00 - 12:20 (C E1-60)



CMPUT 499 - Topics in Computing Science

★ 3 (fi 6)(VAR, VARIABLE)

This topics course is designed for a one on one individual study course between a student and an instructor. Prerequisites are determined by the instructor in the course outline. See Note (3) above.

LECTURE B06 (50301)



CMPUT 566 - Topics in Computing Science

★ 3 (fi 6)(VAR, VARIABLE)

LECTURE B1 (41239)

2023-01-05 - 2023-04-12
TH 11:00 - 12:20 (C E1-60)



CMPUT 605 - Topics in Computing Science

★ 3 (fi 6)(EITHER, 3-0-0)

LECTURE B14 (44649)

LECTURE B40 (50290)