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ML05 · Probabilistic Machine Learning

A free, illustrated L3 course of 11 modules in the Machine Learning track. Modules include: The probabilistic modeling workflow; Bayesian linear regression; Directed graphical models.

ML05 L3 · Advanced 11 modules · about 44 lessons Planned · Ring 3

Part of the Machine Learning track. Ships in Ring 3 (Breadth) of the roadmap.

Modules

1. The probabilistic modeling workflow
2. Bayesian linear regression
3. Directed graphical models
4. Undirected models and factor graphs
5. Exact inference and message passing
6. Hidden Markov models
7. Latent variable models and EM
8. Gaussian processes
9. Sampling-based inference
10. Variational inference
11. Bayesian model selection and Bayesian optimization

Signature illustrations

The pictures a learner should still see in their head a year later:

A primer on plate notation, message-passing animations, fans of Gaussian-process samples before and after data, the HMM trellis, the ELBO gap.

Status

This course is planned, not written yet. It ships with Ring 3 (Breadth).

Every lesson will follow the same twelve-part blueprint with at least three purpose-made figures. Watch the repository to hear when modules ship, or help write and review it.