1 Intermediate ML

This is a work-in-progress draft of intermediate-level machine learning materials. Thanks to LLMs for the high quality; any errors are mine.

Topics:

• Information theory
• Maximum likelihood and maximum a posteriori estimation
• Regularization
• Gaussian linear regression
• Gaussian nonlinear regression
• Quantile regression
• Density estimation
• Exponential family
• Generalized linear models
• Generalized additive models
• Bayesian models
• Principal component analysis

For reference:

• Linear algebra
• Probability distributions