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Machine Learning (McGraw-Hill Series in Computer Science) Hardcover – 4 Jul 1997

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Product details

  • Hardcover: 432 pages
  • Publisher: McGraw-Hill Higher Education (4 July 1997)
  • Language: English
  • ISBN-10: 0070428077
  • ISBN-13: 978-0070428072
  • Product Dimensions: 16 x 2.5 x 24.4 cm
  • Average Customer Review: 5.0 out of 5 stars  See all reviews (6 customer reviews)
  • Amazon Bestsellers Rank: 1,633,228 in Books (See Top 100 in Books)
  • See Complete Table of Contents

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Product Description

From the Publisher

No other book covers the concepts and techniques from the various fields in a unified fashion.
Covers very recent subjects such as genetic algorithms, reinforcement learning, and inductive logic programming.
Writing style is clear, explanatory and precise. --This text refers to the Paperback edition.

From the Author

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.


Inside This Book

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Ever since computers were invented, we have wondered whether they might be made to learn. Read the first page
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Most Helpful Customer Reviews

10 of 10 people found the following review helpful By A Customer on 16 July 1999
Format: Hardcover
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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2 of 2 people found the following review helpful By atgh on 26 Feb. 2012
Format: Paperback
The book covers a broad range of topics and approaches in machine learning. As a consequence, the amount of content dedicated to each topic is quite sparse. Decision Trees, Neural Networks, Bayesian Classifiers/Networks, Instance-Based Learning and Genetic Algorithms are all covered in a single book that counts under 400 pages. Since it is written in a concise and intuitive way however, it provides a solid foundation that the reader can build upon if he wishes to go deeper into any subject. Likewise, with this foundation, readers should be able to easily catch up on recent innovations (the book is quite old). Recommended.
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1 of 2 people found the following review helpful By Nikolas Markou on 17 May 2011
Format: Paperback
This book provides a smooth introduction to Machine Learning. It is not too math heavy and can be used easily by people with math cs background. There little golden nuggets of concentrated experience scattered around which makes it even more worthwhile for people just diving in. Each chapter is independent and straight to the point. I highly recommend it
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