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