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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: 16.3 x 3.3 x 24.1 cm
  • Average Customer Review: 5.0 out of 5 stars  See all reviews (6 customer reviews)
  • Amazon Bestsellers Rank: 1,830,643 in Books (See Top 100 in Books)
  • See Complete Table of Contents

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Product Description

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. The book is intended to support upper level undergraduate and introductory level graduate courses in machine learning.

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.

Inside This Book (Learn More)
First Sentence
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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By Yifei Zhao on 6 Nov 2014
Format: Paperback Verified Purchase
Structure very good. It is very useful for self-study.
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