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These notes for a concise introductory course on probabilistic graphical models. They are based on the Stanford CS228, taught by Stefano Ermon, and are written by Volodymyr Kuleshov, with the help of many students and course staff. You too may help to make these notes better by submitting your improvements to us via Github.
This course starts by introducing probabilistic graphical models from the very basics and concludes by explaining from first principles the variational auto-encoder, an important probabilistic model that is also one of the most influential recent results in deep learning.
