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ermongroup.github.io · 379 words · saved by 3 readers
Lecture notes for Stanford cs228.
These notes form a concise introductory course on probabilistic graphical modelsProbabilistic graphical models are a subfield of machine learning that studies how to describe and reason about the world in terms of probabilities.. They are based on Stanford CS228, and are written by Volodymyr Kuleshov and Stefano Ermon, with the help of many students and course staff. ⊕The notes are still under construction! Although we have written up most of the material, you will probably find several typos. If you do, please let us know, or submit a pull request with your fixes to our GitHub repository.…
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