12 books in series

Adaptive Computation and Machine Learning

Adaptive Computation and Machine Learning is a 11-book series with 12 primary works first released in 1993 with contributions by Peter Spirtes, Clark Glymour, Richard Scheines, Pierre Baldi, Søren Brunak, Ethem Alpaydin, Carl Edward Rasmussen, Christopher K. I. Williams, Daphne Koller, Nir Friedman, Robert E. Schapire, Yoav Freund, Mehryar Mohri, Afshin Rostamizadeh, Ameet Talwalkar, Ian Goodfellow, Yoshua Bengio, Aaron Courville, Kevin P. Murphy, Marc G. Bellemare, Will Dabney, and Mark Rowland.

Deep Learning

2016 • 18 Readers • 800 pages • 4.8

Cover 1

1993 • 1 Reader

Machine Learning: A Probabilistic Perspective

4 Readers

Cover 0

2004 • 1 Reader • 584 pages

Gaussian Processes for Machine Learning

2005 • 248 pages

Probabilistic Machine Learning: An Introduction

2022 • 1 Reader • 858 pages

Boosting: Foundations and Algorithms

2012 • 1 Reader • 544 pages

Distributional Reinforcement Learning

2023 • 1 Reader • 385 pages

Bioinformatics: The Machine Learning Approach

1998 • 1 Reader • 492 pages

Introduction to Statistical Relational Learning

2007 • 1 Reader • 602 pages

Foundations of Machine Learning

2012 • 1 Reader • 427 pages

Probabilistic Graphical Models: Principles and Techniques

2009 • 1,268 pages