Probabilistic Machine Learning: Advanced Topics – Draft

Description

We assume the reader has some prior exposure to (supervised) ML and other relevant mathematical topics (e.g., probability, statistics, linear algebra, optimization). This background material is covered in the prequel to this book, [Probabilistic Machine Learning: An introduction], although the current book is self-contained, and does not require that you read [Probabilistic Machine Learning: An introduction] first.

Since this book cover so many topics, it was not possible to fit all of the content into these pages. Some of the extra material can be found in an online supplement at probml.ai. This site also contains Python code for reproducing most of the figures in the book. In addition, because of the broad scope of the book, about one third of the chapters are written, or co-written, with guest authors, who are domain experts. I hope that by collecting all this material in one place, new ML researchers will find it easier to “see the wood for the trees”, so that we can collectively advance the field using a larger step size.