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.
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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.
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.
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First Sentence
Ever since computers were invented, we have wondered whether they might be made to learn. Read the first page
Front Cover | Copyright | Table of Contents | Excerpt | Index | Back Cover
Ever since computers were invented, we have wondered whether they might be made to learn. Read the first page
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Concordance
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Front Cover | Copyright | Table of Contents | Excerpt | Index | Back Cover
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Customer Reviews
Most Helpful Customer Reviews
9 of 9 people found the following review helpful
By A Customer
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.
1 of 1 people found the following review helpful
By atgh
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.
1 of 2 people found the following review helpful
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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Most Recent Customer Reviews
5.0 out of 5 stars
Complete Coverage of the topic
Covers Machine Learning concepts thoroughly, allowing you to decide which type is best for any particular problem. Read more
Published on 20 May 2001 by Oli
5.0 out of 5 stars
This book has proselytized me!!!!
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). Read more
Published on 10 May 1999
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