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Introduction to Machine Learning (Adaptive Computation and Machine Learning series) second edition Edition

3.6 out of 5 stars 28 customer reviews
ISBN-13: 978-0262012430
ISBN-10: 026201243X
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Editorial Reviews

Review

"A few years ago, I used the first edition of this book as a reference book for a project I was working on. The clarity of the writing, as well as the excellent structure and scope, impressed me. I am more than pleased to find that this second edition continues to be highly informative and comprehensive, as well as easy to read and follow." Radu State Computing Reviews

About the Author

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

  • Series: Adaptive Computation and Machine Learning series
  • Hardcover: 584 pages
  • Publisher: The MIT Press; second edition edition (December 4, 2009)
  • Language: English
  • ISBN-10: 026201243X
  • ISBN-13: 978-0262012430
  • Product Dimensions: 8 x 0.9 x 9 inches
  • Shipping Weight: 2.7 pounds
  • Average Customer Review: 3.6 out of 5 stars  See all reviews (28 customer reviews)
  • Amazon Best Sellers Rank: #677,352 in Books (See Top 100 in Books)

More About the Author

Ethem ALPAYDIN is Professor in the Department of Computer Engineering, Bogazici University, Istanbul Turkey and is a member of the Science Academy, Istanbul. He received his PhD from the Ecole Polytechnique Fédérale de Lausanne, Switzerland in 1990 and was a postdoc at the International Computer Science Institute, Berkeley in 1991. He was a Fulbright scholar in 1997. He was a visiting researcher at MIT, USA in 1994, IDIAP, Switzerland in 1998 and TU Delft, The Netherlands in 2014.

Customer Reviews

Top Customer Reviews

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!
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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.
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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.
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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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Format: Hardcover
I have a little knowledge about some areas of Machine Learning; I have found this book to be a very useful reference for the areas that I am not familiar with.

Explanations are very clear with a very nice examples and illustrations; author also provides good references if deeper understanding of the topic is desired.

Each chapter has a notes section which I found particularly useful, since it gives a brief overview of the field with good references.

Author nicely ties all of the topics together so a more deeper and wholesome understanding could be obtained.

I would highly recommend this book to both undergraduate and graduate students who are interested in Machine Learning.

P.S. I am a PhD candidate in Computer Science.
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Format: Hardcover Verified Purchase
I bought this for use as a reference book rather than a textbook. I found it quite useful with just one proviso: the mathematical presentation goes very fast in places and may be too concise for some readers.
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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.
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