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Introduction to Machine Learning Hardcover – Oct 1 2004


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Product Details

  • Hardcover: 445 pages
  • Publisher: The MIT Press; 1 edition (Oct. 1 2004)
  • Language: English
  • ISBN-10: 0262012111
  • ISBN-13: 978-0262012119
  • Product Dimensions: 17.8 x 2.5 x 22.9 cm
  • Shipping Weight: 930 g
  • Average Customer Review: 3.0 out of 5 stars  See all reviews (1 customer review)
  • Amazon Bestsellers Rank: #741,707 in Books (See Top 100 in Books)

Product Description

About the Author

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

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0 of 3 people found the following review helpful By Jean-philippe Jalbert on Feb. 11 2010
Format: Hardcover
This book is hard to read, because there are so many formulas, it gives headache... But if you like a lot books with formulas, I would recommand it.
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Most Helpful Customer Reviews on Amazon.com (beta)

Amazon.com: 28 reviews
35 of 40 people found the following review helpful
Superb Organization of Ideas! Nov. 17 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!
20 of 25 people found the following review helpful
Good one to start Dec 14 2005
By Subrat Nanda - Published on Amazon.com
Format: Hardcover
I would like to congratulate the author on writing this book, which is crisp and covers whole range of topics. What I liked the most is a systematic disucssion on a wide variety of areas in machine learning with a certain degree of details.

But at the same time, I will also say that the book at some places,(for eg the treatment of Multi Dimensional scaling and Linear discriminants analysis,) lacks depth in its derivations. Also if some explanatory examples are put,it would help the reader, who is doing a first time reading, in understanding the concepts.

At the same time, I think the book achieves it's target of introducing to the reader, a whole gamet of techniques, at a fairly reasonable level. The book is no doubt, a nice and one-stop quick reference for many topics, as such. A commendable thing is an up to date errata maintained by the author, with latest editions made. I would recommend the book for a quick introduction to the subject.
4 of 4 people found the following review helpful
The worst machine learning textbook I ever read April 9 2014
By Wei - Published on Amazon.com
Format: Hardcover
I have read Bishop's PRML, Abu-Mostafa's Learning from Data, Murphy's MLAPP, before I read Introduction to Machine Learning. This book is worst. Poor organization, no details about PAC. It just throws words, ill-definition, no further explanation. The symbols are not standard. They are extremely weird. For example, the book mentioned VC dimension, but it does not give why and how VC is used. Some reasoning is also ridiculous. Cross-validation follows bias-variance section. The book seems to say, since we cannot estimate bias and variance of one model, we need cross-validation to estimate the ability of generalization. It misses the bridge between Bias-variance decomposition and generalization. Do not buy it.

Update, I found several mistakes on that book, the recent one is about kernel machines. The author don't have a clear knowledge of convex optimization. P312 said, this is a convex problem, because the linear constraints are also convex. Wrong. because the inequality constraints are convex. See Boyd's Convex Optimization.

Do not buy it.
16 of 21 people found the following review helpful
Light on detail and lacking solutions July 8 2011
By Robbie Clarken - Published on Amazon.com
Format: Hardcover
Nice breadth of examples of machine learning techniques but light on detail making implementation of the techniques difficult.

There are no solutions to exercises available (except to instructors) so not a good book for self-learners.

I recommend Pattern Recognition and Machine Learning by Christopher M. Bishop instead.
10 of 13 people found the following review helpful
Great book for Learning Machine Learning Oct. 16 2011
By hakan - Published on Amazon.com
Format: Hardcover Verified Purchase
This book is perfect for both the self-learners that like to learn from scratch and for the ones who need to know crucial details of a method in order to use it as a tool. Compared to 'Pattern Classification by Duda, Hart, and Stork', this book has a good balance between providing equations and explaining the idea behind the method. One thing that I like is that the author usually derives the equations. For example, I used the book to implement Hidden Markov Models algorithm in Java for classification. Especially, if you need a good source to learn Support Vector Machines, 'Chapter 10 Linear Discrimination' and 'Chapter 13 Kernel Machines' are the best of their kinds in the Machine Learning literature. Furthermore, examples shown in the figures are unique and very helpful to understand the topic. The author covers some methods that you usually see in the papers but not in the textbooks. Therefore, the book is also a good survey of Machine Learning techniques. In a nutshell, a great resource for those who want to use Machine Learning Algorithms for classification or regression as a tool and for those who want to implement Machine Learning Algorithms in their applications.


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