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Statistical Machine Learning CMPT 727 (3)
Statistical foundation for machine learning algorithms, emphasizing bias-variance tradeoff. Students will learn principles for choosing effective methods and tailoring them to fit a given learning problem. Potential topics include probabilistic graphical models, maximum likelihood estimation, latent variables and the EM algorithm, convex optimization, and variational and sampling-based methods.
| Section | Instructor | Day/Time | Location |
|---|---|---|---|
|
Maxwell Libbrecht |
Jan 6 – Apr 9, 2020: Tue, 11:30 a.m.–12:20 p.m.
Jan 6 – Apr 9, 2020: Thu, 9:30–11:20 a.m. |
Burnaby Burnaby |