Adaptive Computation and Machine Learning Series
Francis Bach, editor
Bioinformatics: The Machine Learning Approach
Pierre Baldi and Søren Brunak, 1998Reinforcement Learning: An Introduction
Richard S. Sutton and Andrew G. Barto, 1998Graphical Models for Machine Learning and Digital Communication
Brendan J. Frey, 1998Learning in Graphical Models
Edited by Michael I. Jordan, 1999Causation, Prediction, and Search, second edition
Peter Spirtes, Clark Glymour, and Richard Scheines, 2000Principles of Data Mining
David J. Hand, Heikki Mannila, and Padhraic Smyth, 2000Bioinformatics: The Machine Learning Approach, second edition
Pierre Baldi and Søren Brunak, 2001Learning Kernel Classifiers: Theory and Algorithms
Ralf Herbrich, 2002Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond
Bernhard Schölkopf and Alexander J. Smola, 2002Introduction to Machine Learning
Ethem Alpaydın, 2004Gaussian Processes for Machine Learning
Carl Edward Rasmussen and Christopher K. I. Williams, 2006Semi-Supervised Learning
Edited by Olivier Chapelle, Bernhard Schölkopf, and Alexander Zien, 2006The Minimum Description Length Principle
Peter D. Grünwald, 2007Introduction to Statistical Relational Learning
Edited by Lise Getoor and Ben Taskar, 2007Probabilistic Graphical Models: Principles and Techniques
Daphne Koller and Nir Friedman, 2009Introduction to Machine Learning, second edition
Ethem Alpaydın, 2010Machine Learning in Non-Stationary Environments: Introduction to Covariate Shift Adaptation
Masashi Sugiyama and Motoaki Kawanabe, 2012Boosting: Foundations and Algorithms
Robert E. Schapire and Yoav Freund, 2012Foundations of Machine Learning
Mehryar Mohri, Afshin Rostamizadeh, and Ameet Talwalker, 2012Machine Learning: A Probabilistic Perspective
Kevin P. Murphy, 2012Introduction to Machine Learning, third edition
Ethem Alpaydın, 2014Deep Learning
Ian Goodfellow, Yoshua Bengio, and Aaron Courville, 2017Elements of Causal Inference: Foundations and Learning Algorithms
Jonas Peters, Dominik Janzing, and Bernhard Schölkopf, 2017Machine Learning for Data Streams, with Practical Examples in MOA
Albert Bifet, Ricard Gavaldà, Geoffrey Holmes, Bernhard Pfahringer, 2018Reinforcement Learning: An Introduction, second edition
Richard S. Sutton and Andrew G. Barto, 2018Foundations of Machine Learning, second edition
Mehryar Mohri, Afshin Rostamizadeh, and Ameet Talwalker, 2019Introduction to Natural Language Processing
Jacob Eisenstein, 2019Introduction to Machine Learning, fourth edition
Ethem Alpaydın, 2020Knowledge Graphs: Fundamentals, Techniques, and Applications
Mayank Kejriwal, Craig A. Knoblock, and Pedro Szekely, 2021Probabilistic Machine Learning: An Introduction
Kevin P. Murphy, 2022Machine Learning from Weak Supervision: An Empirical Risk Minimization Approach
Masashi Sugiyama, Han Bao, Takashi Ishida, Nan Lu, Tomoya Sakai, and Gang Niu, 2022Introduction to Online Convex Optimization, second edition
Elad Hazan, 2022Distributional Reinforcement Learning
Marc G. Bellemare, Will Dabney, and Mark Rowland, 2023Probabilistic Machine Learning: Advanced Topics
Kevin P. Murphy, 2023Foundations of Computer Vision
Antonio Torralba, Phillip Isola, and William T. Freeman, 2024