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, Ada Brunstein adab@mit.edu.

	Bioinformatics The Machine Learning Approach Pierre Baldi and Søren Brunak Cloth / February 1998 OUT OF PRINT
	Bioinformatics, 2nd Edition The Machine Learning Approach Pierre Baldi and Søren Brunak A guide to machine learning approaches and their application to the analysis of biological data. Cloth / August 2001 Price $65.00 | ADD TO CART
	Causation, Prediction, and Search, 2nd Edition Peter Spirtes, Clark Glymour and Richard Scheines 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. Cloth / January 2001 Price $65.00 | ADD TO CART
	Gaussian Processes for Machine Learning Carl Edward Rasmussen and Christopher K. I. Williams A comprehensive and self-contained introduction to Gaussian processes, which provide a principled, practical, probabilistic approach to learning in kernel machines. Cloth / December 2005 Price $36.00 | ADD TO CART
	Graphical Models for Machine Learning and Digital Communication Brendan J. Frey Cloth / June 1998 Price $40.00 | ADD TO CART
	Introduction to Machine Learning Ethem Alpaydin An introductory text in machine learning that gives a unified treatment of methods based on statistics, pattern recognition, neural networks, artificial intelligence, signal processing, control, and data mining. Cloth / October 2004 Price $54.00 | ADD TO CART
	Introduction to Machine Learning, Second Edition Ethem Alpaydin A new edition of an introductory text in machine learning that gives a unified treatment of machine learning problems and solutions. Cloth / February 2010 Price $55.00 | PREORDER
	Introduction to Statistical Relational Learning 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. Cloth / November 2007 Price $53.00 | ADD TO CART
	Learning in Graphical Models Edited by Michael I. Jordan Paper / November 1998 Price $75.00 | ADD TO CART
	Learning Kernel Classifiers Theory and Algorithms Ralf Herbrich An overview of the theory and application of kernel classification methods. Cloth / December 2001 Price $47.00 | ADD TO CART
	Learning with Kernels Support Vector Machines, Regularization, Optimization, and Beyond Bernhard Schölkopf and Alexander J. Smola A comprehensive introduction to Support Vector Machines and related kernel methods. Cloth / December 2001 Price $75.00 | ADD TO CART
	The Minimum Description Length Principle Peter D. Grünwald; Foreword by Jorma Rissanen 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. Cloth / June 2007 Price $47.00 | ADD TO CART
	Principles of Data Mining David J. Hand, Heikki Mannila and Padhraic Smyth The first truly interdisciplinary text on data mining, blending the contributions of information science, computer science, and statistics. Cloth / August 2001 Price $68.00 | ADD TO CART
	Probabilistic Graphical Models Principles and Techniques Daphne Koller and Nir Friedman A general framework for constructing and using probabilistic models of complex systems that would enable a computer to use available information for making decisions. Cloth / August 2009 Price $95.00 | ADD TO CART
	Reinforcement Learning An Introduction Richard S. Sutton and Andrew G. Barto 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. Cloth / March 1998 Price $63.00 | ADD TO CART
	Semi-Supervised Learning Edited by Olivier Chapelle, Bernhard Schölkopf and Alexander Zien 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. Cloth / September 2006 Price $52.00 | ADD TO CART
	Semi-Supervised Learning Edited by Olivier Chapelle, Bernhard Schölkopf and Alexander Zien 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. Paper / March 2010 Price $26.00 | NOT YET AVAILABLE FOR ORDERING