BBiNaWzK7Rw/Dl/aFzhy3w==;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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