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Adaptive Computation and Machine Learning

The goal of building systems that can adapt to their environments and learn from their experience has attracted researchers from many fields, including computer science, engineering, mathematics, physics, neuroscience, and cognitive science. Out of this research has come a wide variety of learning techniques, including methods for learning decision trees, decision rules, neural networks, statistical classifiers, and probabilistic graphical models. The researchers in these various areas have also produced several different theoretical frameworks for understanding these methods, such as computational learning theory, Bayesian learning theory, classical statistical theory, minimum description length theory, and statistical mechanics approaches. These theories provide insight into experimental results and help to guide the development of improved learning algorithms. A goal of the series is to promote the unification of the many diverse strands of machine learning research and to foster high quality research and innovative applications. This series will publish works of the highest quality that advance the understanding and practical application of machine learning and adaptive computation. Research monographs, introductory and advanced level textbooks, how-to books for practitioners will all be considered. For information on the submission of proposals and manuscripts, please contact any of the series editors above or the publisher, Marie Lee ( The series editor is Thomas G. Dietterich, and the associate series editors are Christopher M. Bishop, David Herckerman, Michael I. Jordan, and Michael Kerns.

A substantially revised third edition of a comprehensive textbook that covers a broad range of topics not often included in introductory texts.

Foundations and Algorithms

An accessible introduction and essential reference for an approach to machine learning that creates highly accurate prediction rules by combining many weak and inaccurate ones.

A Probabilistic Perspective

A comprehensive introduction to machine learning that uses probabilistic models and inference as a unifying approach.

Fundamental topics in machine learning are presented along with theoretical and conceptual tools for the discussion and proof of algorithms.

Introduction to Covariate Shift Adaptation

Theory, algorithms, and applications of machine learning techniques to overcome “covariate shift” non-stationarity.

A comprehensive review of an area of machine learning that deals with the use of unlabeled data in classification problems: state-of-the-art algorithms, a taxonomy of the field, applications, benchmark experiments, and directions for future research.

A new edition of an introductory text in machine learning that gives a unified treatment of machine learning problems and solutions.

Principles and Techniques

A general framework for constructing and using probabilistic models of complex systems that would enable a computer to use available information for making decisions.

Edited by Lise Getoor and Ben Taskar

Advanced statistical modeling and knowledge representation techniques for a newly emerging area of machine learning and probabilistic reasoning; includes introductory material, tutorials for different proposed approaches, and applications.

A comprehensive introduction and reference guide to the minimum description length (MDL) Principle that is accessible to researchers dealing with inductive reference in diverse areas including statistics, pattern classification, machine learning, data mining, biology, econometrics, and experimental psychology, as well as philosophers interested in the foundations of statistics.

A comprehensive and self-contained introduction to Gaussian processes, which provide a principled, practical, probabilistic approach to learning in kernel machines.

Theory and Algorithms

An overview of the theory and application of kernel classification methods.

Support Vector Machines, Regularization, Optimization, and Beyond

A comprehensive introduction to Support Vector Machines and related kernel methods.

The first truly interdisciplinary text on data mining, blending the contributions of information science, computer science, and statistics.

The Machine Learning Approach

A guide to machine learning approaches and their application to the analysis of biological data.

The authors address the assumptions and methods that allow us to turn observations into causal knowledge, and use even incomplete causal knowledge in planning and prediction to influence and control our environment.

An Introduction

Richard Sutton and Andrew Barto provide a clear and simple account of the key ideas and algorithms of reinforcement learning. Their discussion ranges from the history of the field's intellectual foundations to the most recent developments and applications.