This course expands on machine learning fundamentals 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. Credit cannot be obtained for both CMPUT 467 and 567 or CMPUT 367 and 567.
Section | Capacity | Class times | Login to view Instructor(s) and Location |
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LECTURE B1
(87865) |
25 |
2026-01-05 - 2026-04-10 (TR)
15:30 - 16:50
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