Machine Learning / Edition 1

by Thomas Mitchell

Overview

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

Product Details

ISBN-13:	9780070428072
Publisher:	McGraw-Hill Higher Education
Publication date:	03/01/1997
Series:	McGraw-Hill Series in Computer Science
Edition description:	Net
Pages:	432
Product dimensions:	6.60(w) x 9.50(h) x 1.30(d)

Table of Contents

Preface xv
Acknowledgments xvi 1 Introduction 1
2 Concept Learning and the General-to-Specific Ordering 20
3 Decision Tree Learning 52
4 Artificial Neural Networks 81

Editorial Reviews

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 intelligence, 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 exercises. 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 undergraduates and graduate students in computer science, engineering, statistics, and social sciences, as well as software professionals. Annotation c. by Book News, Inc., Portland, Or.

Booknews