lRNTrH/CPruWu3lvHxHLSg==;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Structure (classical math reference)
- Ch 1 — Introduction
- Ch 2 — Probability
- Ch 3 — Generative models for discrete data
- Ch 4 — Gaussian models
- Ch 5 — Bayesian statistics
- Ch 6 — Frequentist statistics
- Ch 7 — Linear regression
- Ch 8 — Logistic regression
- Ch 9 — Generalized linear models & the exponential family
- Ch 10 — Directed graphical models (Bayes nets)
- Ch 11 — Mixture models & EM
- Ch 12 — Latent linear models
- Ch 13 — Sparse linear models
- Ch 14 — Kernels
- Ch 15 — Gaussian processes
- Ch 16 — Adaptive basis function models
- Ch 17 — Markov and hidden Markov models
- When to open this PDF
- When NOT to open this PDF
- Quick cross-reference to lessons