by Kevin Patrick Murphy.

MIT Press, 2023.

Key links

• Short table of contents
• 
  Long table of contents

• Preface
• 
  Draft pdf of the main book, 2023-08-15. CC-BY-NC-ND license. (Please cite the official reference below.)

• Supplementary material
• Issue tracker.
• 
  Code to reproduce most of the figures

• Acknowledgements
• Endorsements

If you use this book, please be sure to cite

 @book{pml2Book,
 author = "Kevin P. Murphy",
 title = "Probabilistic Machine Learning: Advanced Topics",
 publisher = "MIT Press",
 year = 2023,
 url = "http://probml.github.io/book2"
}

Downloads since 2022-02-28.

Table of contents

Endorsements

• “Kevin Murphy has distilled the vast and confusing literature on machine learning and neural networks
  into a beautifully written
  and extremely clear textbook that will be a wonderful resource for both new students
  and seasoned researchers who are trying to keep up with this fast moving field.
  The chapter on generative models is a masterpiece.”
  — Geoff Hinton, U. Toronto/ Google.

• “Kevin Murphy had already impressed and greatly benefited the machine learning community with his introductory
  book on probabilistic ML
  and I am delighted to see the depth and breadth of material in his new sequel on advanced probabilistic ML.
  The book covers topics which I believe are at the heart of past and upcoming advances in our field,
  while often lacking in the training of graduate students in computer science,
  and I therefore recommend it highly to all of them.”
  — Yoshua Bengio, U. Montreal

• “This book is an amazing tour de force: Murphy and his co-authors have described and systematized virtually
  all of the important advances in machine learning over the past 30 years. Pick any topic, and they provide a
  crisp description of the state-of-the-art methods in a common, well-chosen notation and using a set of core concepts
  in modeling, statistics, and optimization. This book will be a valuable starting point for students entering
  the field and a wonderful reference for seasoned researchers. It is hard to imagine a better antidote to the vast,
  confusing, and voluminous literature in machine learning. This is such an amazing book! I learned things even in the sections
  where I’m fairly up to date with the literature. ” — Tom Dietterich,
  Oregon State University

• “The prior version of Dr. Murphy’s book was amongst the 3-4 books I recomme