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Pattern Recognition and Machine Learning (Information Science and Statistics)

by Christopher Bishop (Author)
4.1 out of 5 stars 125 customer reviews
ISBN-13: 978-0387310732
ISBN-10: 0387310738
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Frequently Bought Together

  • Pattern Recognition and Machine Learning (Information Science and Statistics)
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  • The Elements of Statistical Learning: Data Mining, Inference, and Prediction, Second Edition (Springer Series in Statistics)
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  • Machine Learning: A Probabilistic Perspective (Adaptive Computation and Machine Learning series)
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Product Details

  • Series: Information Science and Statistics
  • Hardcover: 738 pages
  • Publisher: Springer (October 1, 2007)
  • Language: English
  • ISBN-10: 0387310738
  • ISBN-13: 978-0387310732
  • Product Dimensions: 1.8 x 7.2 x 9.2 inches
  • Shipping Weight: 4.1 pounds (View shipping rates and policies)
  • Average Customer Review: 4.1 out of 5 stars  See all reviews (125 customer reviews)
  • Amazon Best Sellers Rank: #13,517 in Books (See Top 100 in Books)

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Customer Reviews

Top Customer Reviews

3.0 out of 5 starsGreat Insights, but a hard read
By Sidhant on June 16, 2007
Format: Hardcover Verified Purchase
This new book by Chris Bishop covers most areas of pattern recognition quite exhaustively. The author is an expert, this is evidenced by the excellent insights he gives into the complex math behind the machine learning algorithms. I have worked for quite some time with neural networks and have had coursework in linear algebra, probability and regression analysis, and found some of the stuff in the book quite illuminating.

But that said, I must point out that the book is very math heavy. Inspite of my considerable background in the area of neural networks and statistics, I still was struggling with the equations. This is certainly not the book that can teach one things from the ground up, and thats why I would give it only 3 stars. I am new to kernels, and I am finding the relevant chapters difficult and confusing. This book wont be very useful if all you want to do is write machine learning code. The intended audience for this book I guess are PhD students/researchers who are working with the math related aspects of machine learning. Undergraduates or people with little exposure to machine learning will have a hard time with this book. But that said, time spent in struggling with the contents of this book will certainly pay-off, not instantly though.
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3.0 out of 5 starsconcentrates too much on the easy stuff
By _claudia_ on July 9, 2008
Format: Hardcover
The book is worth a look, but after some of 5 star reviews i read here, it was quite a disappointment. Yes, the book covers a lot of ground. Yes, the book has lots of nice pictures and easy examples, but that is exactly the problem. There are lots and lots of simple examples to explain the most basic concepts, but when it gets complicated the book often sounds as if the text was taken out of a mathematics book. For example: the basics of probability theory are introduced for over 5 pages with the example of "two coloured boxes each containing fruit". Nothing wrong with that. Then the chapter continues with probability densities which are covered within 2 pages and contain sentences like "Under a nonlinear change of variable, a probability density transforms differently from a simple function, due to the Jacobian factor". There is no mentioning how a simple function exactly transforms, what a Jacobian factor actually is and why we would be interested in a nonlinear change. Surely, some of the introductory pages could have been thrown out to explain in depth the more difficult issues. Unfortunately, this is not the only time, where easy concepts get a lot of attention and the truly important complex concepts are skimmed over. All in all, still worth a read, though do not expect too much.
8 Comments 147 people found this helpful. Was this review helpful to you? Yes No Sending feedback...
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2.0 out of 5 starsThorough but vastly unclear
By dc on February 27, 2007
Format: Hardcover Verified Purchase
I can appreciate others who might think that this is a great book.... but I am a student using it and I have some very different opinions of it.

First, although Mr. Bishop is clearly an expert in Machine Learning, he is also obviously a HUGE fan of Bayesian Statistics. The title of the book is misleading as it makes no mention of Bayes at all but EVERY CHAPTER ends with how all of the chapter's contents are combined in a Bayes method. That's not bad it's just not clear from the title. The title should be appended with "... using Bayesian Methods"

Second, while it is certainly a textbook, the author clearly has an understanding of the material that seems to undermine his ability to explain it. Though there are mentions of examples there are, in fact, none. There are many graphics and tiny, trivial indicators, but I can't help to think that every single one of the concepts in the book would have benefited from even a single application. There aren't any. I am lead to believe that if you are already aware of many of the methods and techniques that this would be an excellent reference or refresher. As a student starting out I almost always have no idea what his intentions are.

To make matter worse, he occasionally uses symbols that are flat-out confusing. Why would you use PI for anything other than Pi or Product? He does. Why use little k, Capital K, and Greek Letter Kappa (a K!) in a series of explanations. He does. He even references articles that he has written... in 2008!!

Every chapter seems to be an exercise to see how many equations he can stuff in it. There are 300 in Chapter 2 alone.Read more ›
10 Comments 364 people found this helpful. Was this review helpful to you? Yes No Sending feedback...
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3.0 out of 5 starsThe book should change its title
By John E on September 25, 2007
Format: Hardcover Verified Purchase
This book (PRML) should be re-titled as "PRML: a bayesian approach". Yes, bayesian approach is very useful for machine learning, and sometimes the final goal of learning is to maximize some sort of posterior probability. However, if the author is such a huge fun of bayes statistics, please tell perspective readers in a clear way. Emphasize bayes aspects too much really hurt the quality of this book as a general-purpose textbook of machine learning.

For a better textbook of machine learning, I recommend:
1) The elements of statistical learning (perhaps this book a little hard for beginner in this field -- but as least better than PRML -- you can compare their chapters about linear regression to see which one is better).
2) Pattern classification (focus on classification, not regression. Also not very easy -- anyway, machine learning is not an easy field ^_^).
3) Machine Learning (a little old, but great for beginner.)

These three book also mention bayesian statistics, but in a proper way. If you have some experience in machine learning and have engineering-level math background, just choose the 1) or 2). If you are completely a beginner, first take a glance on 3), and then go to 1) or 2).

Finally, if you want a book that discusses machine learning purely from bayesian perspective, PRML is good.
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Most Recent Customer Reviews

5.0 out of 5 starsMust have for machine learning library

One of my top references along with The Elements of Statistical Learning and Kevin Murphy's book.

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Authoritative reference ... though very dense on information but very readable ...

Published 7 months ago by mehdi
5.0 out of 5 starsGood intro book

This is a good intro book - wide coverage of topics.

Published 11 months ago by Renat Bekbolatov
5.0 out of 5 starsBest machine learning book for people without a formal math background

I do not have a formal math background and I am able to follow this book. It is very easy to understand for a self motivated learner. Read more

Published 12 months ago by Brennan C
5.0 out of 5 starsFive Stars

Everything fine, thanks!

Published 13 months ago by Benjamin Ellenberger
3.0 out of 5 starsok book, not great

I have two machine learning books currently; this book and the one by Prince on Computer Vision. I purchased Prince first, and followed up with this book, since in some ways I... Read more

Published 14 months ago by A. Stewart
3.0 out of 5 starsOverall good, lots of potential for improvement

Overall, I like the book and would recommend it. But the exposition could use significant polishing. Read more

Published 17 months ago by IB
5.0 out of 5 starsgood for the course and research

A compulsory book required by statistical machine learning, good for the course and research, but not recommend for practical machine learning.

Published 18 months ago by Bo

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