From Book News, Inc. An introductory text on primary approaches to machine learning and the study of computer algorithms that improve automatically through experience. Introduce basics concepts from statistics, artificial intellig ence, information theory, and other disciplines as need arises, with balanced coverage of theory and practice, and presents major algorithms with illustrations of their use. Includes chapter exerciz ses. Online data sets and implementations of several algorithms are available on a Web site. No prior background in artificial intelligence or statistics is assumed. For advanced undergraduat es and graduate students in computer science, engineering, statistics, and social scien ces, as well as software professionals.
Presents the key algorithms and theory that form the core of machine learning. Discusses such theoretical issues as How does learning performance vary with the number of training examples presented? and Which learning algorithms are most appropriate for various types of learning tasks? DLC: Computer algorithms.
This book covers the field of machin e learning, which is the study of algorithms that allow computer programs to automatically improve through experience. The book is intended to support upper le vel undergraduate and introductory level graduate courses in machine learning