Probabilistic Machine Learning: An Introduction

Key links

• Short table of contents
• Long table of contents
• Preface
• Draft pdf file, CC-BY-NC-ND license. (Please cite the official reference below.)
• Python code
• Issue tracker. Use this to report problems with the book and/or code. Be sure to specify which release (date stamp) of the book you are using. Please specify pdf and print page number (which sometimes differ). For reporting small typos, please collect a batch of errors into a doc, and create a single issue (or add to an existing open issue list).

 @book{pml1Book,
 author = "Kevin P. Murphy",
 title = "Probabilistic Machine Learning: An introduction",
 publisher = "MIT Press",
 year = 2021,
 url = "probml.ai"
}

Table of contents

Endorsements

• since first release in 2020-12-28.

• "Kevin Murphy’s book on machine learning is a superbly written, comprehensive treatment of the field, built on a foundation of probability theory. It is rigorous yet readily accessible, and is a must-have for anyone interested in gaining a deep understanding of machine learning." -- Chris Bishop, Microsoft Research.

• "This is a remarkable book covering the conceptual, theoretical and computational foundations of probabilistic machine learning, starting with the basics and moving seamlessly to the leading edge of this field. The pedagogical structure of the book is extremely useful for teaching. One of my favorite parts is that most of the figures of the book have a link to the associated (python, JAX, tensorflow) code that is used to generate them, often with comparisons between the different computational ways of solving the problems." -- Michael Brenner, Harvard.

• "This book could be titled 'What every ML PhD student should know'. If you master the material in this book, you will have an outstanding foundation for successful research in machine learning.” -- Tom Dietterich, U. Oregon

• "There are many books on machine learning out there, but none gives such a well-rounded, up-to-date, and comprehensive view of the field as this one. We use this book as reference reading for our students taking the advanced machine learning course at Oxford to introduce them to fundamental as well as current topics in the field. I'm amazed at the amount of work that went into this book---which will surely be used by many to train the next generation of machine learning experts." -- Yarin Gal, U. Oxford

• "This is a terrific resource for machine learning students and researchers. If you want to understand the foundations of modern machine learning then this is the book to read. The text is particularly strong at marrying classical ideas from statistics and probability with more modern concepts such as deep learning." -- Padhraic Smyth, UC Irvine

• "My favorite machine learning book just received a face-lift! 'Probabilistic Machine Learning: An Introduction' is the most comprehensive and accessible book on modern machine learning by a large margin. It now also covers the latest developments in deep learning and causal discovery. With this upgrade it will remain the reference book for our field that every respected researcher needs to have on their desk." -- Max Welling, U. Amsterdam

• "Prof Murphy's 2012 book was a triumph, covering both basic material and also the state-of-the-art. The new 'Probabilistic Machine Learning: An Introduction' is similarly excellent, and includes new material, especially on deep learning and recent developments. It will become an essential reference for students and researchers in probabilistic machine learning." -- Chris Williams, U. Edinburgh

Acknowledgements

• People who helped write some sections: Mathieu Blondel, Krzysztof Choromanski, Justin Gilmer, Zico Kolter, Frederick Kunster, Lihong Li, Si Yi Meng, Aaron Mishkin, Byran Perozzi, Colin Raffel, Mark Schmidt, Sharan Vaswani, Andrew Wilson.
• Proof reader: John Fearns.
• People who have provided feedback on parts of the book: Sebastien Bratieres, Kai Brodersen, Peter Cerno, Daniel Galvez, Abhishek Kumar, Max Lepikhin, Aaron Michelony, Horst Stühler, Hal Varian, Clem Wang.
• People who have helped with the code: Andrew Carr, Aurelien Geron, Gerardo Durán Martín, Osvaldo Martin, Duane Rich, Mahmoud Soliman, Theodore Vasiloudis, Oscar Wahltinez.
• People who have helped with the figures: Sandeep Choudhary, and others who are credited in the figure captions.