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Reviews
Editorial Reviews
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 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. Book News, Inc.®, Portland, OR Book Description
This book covers the field of machine learning, which is the study of algorithms that allow computer programs to automatically improve through experience, and that automatically infer general laws from specific data. The book is intended to support upper level undergraduate and introductory level graduate courses in Machine Learning. The author, Tom Mitchell, Tom.Mitchell@cmu.edu , June 29, 1997
Table of Contents:
1. Introduction
2. Concept Learning and General-to-Specific Ordering
3. Decision Tree Learning
4. Artificial Neural Networks
5. Evaluating Hypotheses
6. Bayesian Learning
7. Computational Learning Theory
8. Instance-Based Learning
9. Genetic Algorithms
10. Learning Sets of Rules
11. Analytical Learning
12. Combining Inductive and Analytical Learning
13. Reinforcement Learning Includes web-accessible data and code.
Customer Reviews
Write an online review and share your thoughts with other readers!
Clear, lucid, rigorous,great coverage
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Reviewer:
Steve Bucuvalas
from USA
October 21, 1999
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It is very rare to find a text that both does rigorous justice to a subject, and also is an enjoyable read. This book is such a rarity
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1 people found this review helpful.
0 did not.
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Excellent overview of all major machine learning topics.
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Reviewer:
Dr. John W. Sheppard (jsheppar@arinc.com)
from Annapolis, MD
July 16, 1999
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I first used this book as the required text for my course in ML in 1997 and got rave reviews from the students. I will be using it again in 1999. I found ALL of the major topics and issues in ML addressed. The book is easily readable with anyone with a computer science background, and the book works quite well in a wide variety of approaches to presentation at the advanced undergraduate and graduate levels.
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1 people found this review helpful.
0 did not.
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This book has proselytized me!!!!
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Reviewer:
Munish Sikka (msikka@eds.tamu.edu)
from Texas A&M Univ. College Station. TX.USA
May 9, 1999
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Everything I will do in the future will be based on ML and just one semester of an ML course & this book has converted me(even though my major is not Comp.Science). Of-course this is due majorly to Dr. Thomas Ioerger and his teaching abilities(Texas A&M), but the book presents all concepts(even seemingly complex ones) in a way that is easy and enjoyable to learn. One of the most useful books I've ever studied!
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A great introduction to the field of machine learning!
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Reviewer:
Jason Roberts (imt@jps.net)
from Chicago, IL
May 1, 1999
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This book does an incredible job of presenting sophisticated material in a clear and easy to understand style. I highly recommend it to anyone interested in the field. Absolutely first rate!
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