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Introduction to Machine Learning (Adaptive Computation and Machine Learning) (Adaptive Computation and Machine Learning Series) Hardcover – 22 Jan 2010

3.4 out of 5 stars
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3.4 out of 5 stars 29 customer reviews

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Review

"This volume offers a very accessible introduction to the field of machine learning. Ethem Alpaydin gives a comprehensive exposition of the kinds of modeling and prediction problems addressed by machine learning, as well as an overview of the most common families of paradigms, algorithms, and techniques in the field. The volume will be particularly useful to the newcomer eager to quickly get a grasp of the elements that compose this relatively new and rapidly evolving field." --Joaquin Quinonero-Candela, coeditor, Dataset Shift in Machine Learning

About the Author

Ethem Alpaydin is Professor in the Department of Computer Engineering at Bogazici University, Istanbul.

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Most Helpful Customer Reviews on Amazon.com (beta)

Amazon.com: 3.4 out of 5 stars 29 reviews
2 of 3 people found the following review helpful
5.0 out of 5 stars Wonderful textbook! 28 July 2013
By John T Hill IV - Published on Amazon.com
Format: Hardcover Verified Purchase
This was recommended to me by a colleague. Reading through it has sharpened my understanding in the areas of machine learning that I already knew, and broadened it into the areas that I've only heard colloquially. I would recommend this to anyone aspiring to get a grounding in this broad field.
36 of 42 people found the following review helpful
4.0 out of 5 stars Superb Organization of Ideas! 18 Nov. 2006
By Machine Learner - Published on Amazon.com
Format: Hardcover Verified Purchase
The topics and concepts in this book are exceptionally well organized. After reading it from cover to cover, I could easily see how all the ideas and concepts fit into place. I have two main criticisms. First, the notation is sometimes non-standard, e.g. the r vector is used to denote the label vector and superscripts are used sometimes as subscripts. Second, the explanations are sometimes too brief. For example, when deriving the solution for Least Squares Regression with Quadratic Discriminants, Vandermode matrices are used but the author fails to identify them as such, or to explain why they are useful. If the author were to write an extra sentence on every other page, the explanations would be perfect!
5 of 8 people found the following review helpful
5.0 out of 5 stars Detailed step by step equations with easy to understand text 9 Nov. 2010
By M. Yang - Published on Amazon.com
Format: Hardcover Verified Purchase
I had a bachelor degree in computer science and now I am a student transportation. The textbooks in transportation used a lot of statistic theory, but none of them wrote in a understandable way. Just from the table of content, you will find out maximum likelihood, linear discriminant analysis and principal component analysis, etc. All of them explained in a comprehensible way. It's a good book for those who studied in CS and now want to learn statistic by yourself to process and classify huge amount of data. Highly recommended!.
1 of 3 people found the following review helpful
5.0 out of 5 stars Really great for beginners, but make sure you start at the beginning 22 Feb. 2013
By Hadayat Seddiqi - Published on Amazon.com
Format: Hardcover Verified Purchase
This book is very easy to read, but make sure you start at the beginning. I was able to learn quite a bit about ML in just a month by reading about half of this book. Of course Wiki and slides online will help with that also, but this book was my primary text. Really great for learning on your own.
2 of 4 people found the following review helpful
4.0 out of 5 stars Primary reference book for machine learning techniques 9 April 2013
By Birkan Tunc - Published on Amazon.com
Format: Hardcover Verified Purchase
This book meets all my need for a reference book in machine learning domain. Specifically, the second edition covers all fundamental topics. I usually use this book when starting to learn a new technique.
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