Lecture 01: Review on
probability and statistics
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Lecture 02: Likelihood
models
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accessible HTML
- 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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Accessible HTML is generated by converting the PDF to HTML via pandoc. Gemini AI wrote a simple script for me to run this conversion. See this page for introduction.
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