CMPUT 467 - Machine Learning II

★ 3 (fi 6)(EITHER, 3-0-0)

Faculty of Science

This is the second course of a two-course sequence on machine learning, with a focus on extending to nonlinear modeling with neural networks and higher-dimensional data. Topics include: optimization approaches (constrained optimization, hessians, matrix solutions), deep learning and neural networks, generative models, more advanced methods for assessing generalization (cross-validation, bootstrapping), introduction to non-iid data and missing data. Prerequisites: CMPUT 204 and CMPUT 267; any 300-level Computing Science course; and one of MATH 101, 115, 118, 136, 146, or 156. Credit cannot be obtained in both CMPUT 367 and 467.

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Winter Term 2025


Section Capacity Class times Instructor(s)
2025-01-06 - 2025-04-09 (TR)
15:30 - 16:50
BUS 2-05