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