Pub. Date:
McGraw-Hill Higher Education
Machine Learning / Edition 1

Machine Learning / Edition 1

by Thomas Mitchell
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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

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Machine Learning 3.5 out of 5 based on 0 ratings. 2 reviews.
IvanIdris on LibraryThing More than 1 year ago
This is an introductory book on Machine Learning. There is quite a lot of mathematics and statistics in the book, which I like. A large number of methods and algorithms are introduced: Neural Networks Bayesian Learning Genetic Algorithms Reinforcement LearningThe material covered is very interesting and clearly explained. I find the presentation, however, a bit lacking. I think it has to do with the chosen fonts and lack of highlighting of important terms. Maybe it would have been better to have shorter paragraphs too.If you are looking for an introductory book on machine learning right now, I would not recommend this book, because in recent years better books have been written on the subject. These are better obviously, because they include coverage of more modern techniques. I give this book 3 out of 5 stars.
Anonymous More than 1 year ago