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STAT 535: Statistical Machine Learning (2025 Autumn)

Lecture Notes

Lecture 01: Review on probability and statistics
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Lecture 02: Likelihood models
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- Also see this note.

Lecture 03: Generative models: mixture, variational, and flows
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- Major update: VAE, diffusion model, and normalizing flows.
- Also see this note.

Lecture 04: Linear regression and penalization
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- Minor update: clarifying typos.

Lecture 05: Graph and networks
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Lecture 06: Density estimation
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- Major update: Holder smoothness, derivative, and sampling from KDE.

Lecture 07: Nonparametric regression
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- Major update: Plug-in and local least square methods, general basis regression, neural nets.

Lecture 08: Classification
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Lecture 09: Clustering
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Lecture 10: Dimension reduction
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Lecture 11: Monte Carlo methods
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Lecture 12: The bootstrap
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- Major update: different variants of bootstrap and CI’s, Lindeberg-Feller’s CLT.
- See this R-code generated by AI (Gemini 3.0).

Lecture 13: Missing data
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Lecture 14: Causal inference
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Copyright © 2013-2025 Yen-Chi Chen. All rights reserved.