Covers the field of machine learning, which is the study of algorithms that allow computer programs to automatically improve through experience. This book is intended to support upper level undergraduate and introductory level graduate courses in machine learning.

This edition has been completely revised, enlarged and formatted in two colour. It is a systematic account of the major topics in pattern recognition, based on the fundamental principles. It includes extensive examples and exercises, and is accompanied by a solutions manual.

Covers supervised learning (prediction) to unsupervised learning. This book contains topics including neural networks, support vector machines, classification trees and boosting.

As with any burgeoning technology that enjoys commercial attention, the use of data mining is surrounded by a great deal of hype. This book includes comprehensive information on neural networks. It features a section on Bayesian networks.

The dramatic growth in practical applications for machine learning over the years has been accompanied by many important developments in the underlying algorithms and techniques. This textbook reflects these developments while providing a comprehensive introduction to the fields of pattern recognition and machine learning.

Providing a comprehensive account of neural networks from a statistical perspective, this book emphasizes on pattern recognition, which represents the area of greatest applicability for neural networks in contemporary times.

Demonstrates how you can build Web 2.0 applications to mine the enormous amount of data created by people on the Internet. This book explains how to draw conclusions about user experience, marketing, personal tastes, and human behavior in general - from information collected every day.

A comprehensive introduction to this recent method for machine learning and data mining.

An account of key ideas and algorithms in reinforcement learning. The discussion ranges from the history of the field's intellectual foundations to recent developments and applications. Areas studied include reinforcement learning problems in terms of Markov decision problems and solution methods.

For one or two-semester, undergraduate or graduate-level courses in Artificial Intelligence. The long-anticipated revision of this best-selling text offers the most comprehensive, up-to-date introduction to the theory and practice of artificial intelligence.

Fun and exciting textbook on the mathematics underpinning the most dynamic areas of modern science and engineering.

Unique account of developing topic in data mining and machine learning.

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.

Pattern recognition presents one of the most significant challenges for scientists and engineers, and many different approaches have been proposed. This book presents an account of probabilistic analysis of these approaches. It includes such topics as distance measures, Vapnik-Chervonenkis theory, epsilon entropy, and parametric classification.

Discusses the fundamental ideas which lie behind the statistical theory of learning and generalization. This book considers learning as a general problem of function estimation based on empirical data. It concentrates on discussing the main results of learning theory and their connections to fundamental problems in statistics.

This book is devoted to the statistical theory of learning and generalization, that is, the problem of choosing the desired function on the basis of empirical data. The author will present the whole picture of learning and generalization theory. Learning theory has applications in many fields, such as psychology, education and computer science.

The clearest explanation of the statistical framework for pattern recognition and machine learning, now in paperback.

Examines low-level machine learning techniques as they relate specifically to the challenges of Web mining. This work focuses on applications that unite infrastructure and analysis to bring machine learning to bear on systematically acquired and stored data.

In this book Pierre Baldi and Soren Brunak present the key machine learning approaches and apply them to the computational problems encountered in the analysis of biological data. The book is aimed both at biologists and biochemists who need to understand new data-driven algorithms.

This text takes an interdisciplinary approach to the problems inherent in data mining. Drawing on contributions from information science, computer science and statistics, the book analyzes the difficulties of storing, accessing, modelling and understanding very large data sets.