STAT 535: Statistical Machine Learning (2025 Autumn)

Lecture Notes

Lecture 01: Review on probability and statistics

Lecture 02: Likelihood models
- Also see this note.

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

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

Lecture 05: Graph and networks

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

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

Lecture 08: Classification

Lecture 09: Clustering

Lecture 10: Dimension reduction

Lecture 11: Monte Carlo methods

Lecture 12: The bootstrap
- 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

Lecture 14: Causal inference