These notes form a concise introductory course on probabilistic graphical models. They are based on Stanford CS228, taught by Stefano Ermon, and have been written by Volodymyr Kuleshov, with the help of many students and course staff.
This course starts by introducing graphical models from the very basics and concludes by explaining from first principles the variational auto-encoder.
The compiled version is available here.
This material is under construction! Although we have written up most of it, you will probably find several typos. If you do, please let us know, or submit a pull request with your fixes via Github.
The notes are written in Markdown and are compiled into HTML using Jekyll. Please add your changes directly to the Markdown source code.