Adaptive Computation and Machine Learning
Thomas Dietterich, 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, 2nd ed., Peter Spirtes, Clark Glymour, and Richard Scheines
Principles of Data Mining, David Hand, Heikki Mannila, and Padhraic Smyth
Bioinformatics: The Machine Learning Approach, 2nd ed., Pierre Baldi and Søren Brunak
Learning Kernel Classifiers: Theory and Algorithms, Ralf Herbrich
Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond, Bern-
hard 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
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