ermongroup/cs228-notes resource

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Course notes for Stanford CS228 on probabilistic graphical models, written in Markdown and published as a static website via Jekyll and GitHub Pages. The material covers graphical models from basics through variational auto-encoders.

Use cases

• learn probabilistic graphical models
• study Bayesian networks and Markov random fields
• understand variational inference and VAEs from first principles
• find a free alternative to a PGM textbook
• prepare for a course on graphical models
• reference material for probabilistic machine learning

When to choose

• you want a concise, free introduction to probabilistic graphical models
• you are self-studying Stanford CS228 material
• you need readable notes bridging basics to variational auto-encoders

When to avoid

• you need runnable code or exercises with solutions
• you want an exhaustive textbook-level treatment
• you need interactive notebooks rather than static notes

Facets

learning-resource · maturity stable

documentation machine-learning machine-learning tutorials education python probabilistic-graphical-models course-notes stanford-cs228 jekyll variational-inference bayesian-networks web-server

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