Adaptive Computation and Machine Learning
Francis Bach, Editor
Christopher Bishop, David Heckerman, Michael Jordan, and Michael Kearns, Associate Editors
Bioinformatics: The Machine Learning Approach, Pierre Baldi and Søren Brunak
Reinforcement Learning: An Introduction, Richard S. Sutton and Andrew G. Barto
Graphical Models for Machine Learning and Digital Communication, Brendan J. Frey
Learning in Graphical Models, Michael I. Jordan
Causation, Prediction, and Search, second edition, Peter Spirtes, Clark Glymour, and Richard Scheines
Principles of Data Mining, David Hand, Heikki Mannila, and Padhraic Smyth
Bioinformatics: The Machine Learning Approach, second edition, Pierre Baldi and Søren Brunak
Learning Kernel Classifiers: Theory and Algorithms, Ralf Herbrich
Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond, Bernhard Schölkopf and Alexander J. Smola
Introduction to Machine Learning, Ethem Alpaydin
Gaussian Processes for Machine Learning, Carl Edward Rasmussen and Christopher K.I. Williams
Semi-Supervised Learning, Olivier Chapelle, Bernhard Schölkopf, and Alexander Zien, Eds.
The Minimum Description Length Principle, Peter D. Grünwald
Introduction to Statistical Relational Learning, Lise Getoor and Ben Taskar, Eds.
Probabilistic Graphical Models: Principles and Techniques, Daphne Koller and Nir Friedman
Introduction to Machine Learning, second edition, Ethem Alpaydin
Machine Learning in Non-Stationary Environments: Introduction to Covariate Shift Adaptation, Masashi Sugiyama and Motoaki Kawanabe
Boosting: Foundations and Algorithms, Robert E. Schapire and Yoav Freund
Machine Learning: A Probabilistic Perspective, Kevin P. Murphy
Foundations of Machine Learning, Mehryar Mohri, Afshin Rostami, and Ameet Talwalker
Introduction to Machine Learning, third edition, Ethem Alpaydin
Deep Learning, Ian Goodfellow, Yoshua Bengio, and Aaron Courville
Toward Causal Learning, Jonas Peters, Dominik Janzing, and Bernhard Schölkopf
Machine Learning for Data Streams with Practical Examples in MOA, Albert Bifet, Ricard Gavalda, Geoff Holmes, and Bernhard Pfahringer