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| Pattern Recognition and Machine Learning (Information Science and Statistics) | 
 enlarge | Author: Christopher M. Bishop
Publisher: Springer
Category: Book
List Price: $84.95
Buy New: $58.75
You Save: $26.20 (31%)
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Avg. Customer Rating:  (41 reviews)
Sales Rank: 16261
Languages: English (Original Language), English (Unknown), English (Published)
Media: Hardcover
Edition: 1
Number Of Items: 1
Pages: 738
Shipping Weight (lbs): 4
Dimensions (in): 9.4 x 7.6 x 1.8
ISBN: 0387310738
Dewey Decimal Number: 006.4
EAN: 9780387310732
ASIN: 0387310738
Publication Date: October 1, 2007
Availability: Usually ships in 1-2 business days
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| Customer Reviews:
 not what I had in mind March 18, 2008
5 out of 35 found this review helpful
Once my wife asked me to go and pick up some carpet for the hallway that matched what was in the living room. She was not happy when I came back with totally the wrong pattern. "That's completely the wrong pattern!" she yelled and tossed the cat at me.
When I saw the title of this book, I thought for sure it could help me with my pattern recognizin'. Also, I could use some machine learnin since the vcr clock has been blinking 12:00 since 1982.
Sadly, the book was not at all what I expected.
Also, the pattern on the cover is very ugly.
Very good text, but with some flaws February 22, 2008
9 out of 9 found this review helpful
First of all, as some other reviewers have pointed out, the subtitle of the book should include the word 'Bayesian' in some form or the other. The reason this is important is because the Bayesian approach, although an important one, is not adapted across the board in machine learning, and consequently, an astonishing number of methods presented in the book (Bayesian versions of just about anything) are not mainstream. The recent Duda book gives a better idea of the mainstream in this sense, but because the field has evolved in such rapidity, it excludes massive recent developments in kernel methods and graphical models, which Bishop includes.
Pedagogically, however, this book is almost uniformly excellent. I didn't like the presentation on some of the material (the first few sections on linear classification are relatively poor), but in general, Bishop does an amazing job. If you want to learn the mathematical base of most machine learning methods in a practical and reasonably rigorous way, this book is for you. Pay attention in particular to the exercises, which are the best I've seen so far in such a text; involved, but not frustrating, and always aiming to further elucidate the concepts. If you want to really learn the material presented, you should, at the very least, solve all the exercises that appear in the sections of the text (about half of the total). I've gone through almost the entire text, and done just that, so I can say that it's not as daunting as it looks. To judge your level regarding this, solve the exercises for the first two chapters (the second, a sort of crash course on probability, is quite formidable). If you can do these, you should be fine. The author has solutions for a lot of them on his website, so you can go there and check if you get stuck on some.
As far as the Bayesian methods are concerned, they are usually a lot more mathematically involved than their counterparts, so solving the equations representing them can only give you more practice. Seeing the same material in a different light can never hurt you, and I learned some important statistical/mathematical concepts from the book that I'd never heard of, such as the Laplace and Evidence Approximations. Of course, if you're not interested, you can simply skip the method altogether.
From the preceding, it should be clear that the book is written for a certain kind of reader in mind. It is not for people who want a quick introduction to some method without the gory details behind its mathematical machinery. There is no pseudocode. The book assumes that once you get the math, the algorithm to implement the method should either become completely clear, or in the case of some more complicated methods (SVMs for example), you know where to head for details on an implementation. Therefore, the people who will benefit most from the book are those who will either be doing research in this area, or will be implementing the methods in detail on lower level languages (such as C). I know that sounds offputting, but the good thing is that the level of the math required to understand the methods is quite low; basic probability, linear algebra and multivariable calculus. (Read the appendices in detail as well.) No knowledge is needed, for example, of measure-theoretic probability or function spaces (for kernel methods) etc. Therefore the book is accessible to most with a decent engineering background, who are willing to work through it. If you're one of the people who the book is aimed at, you should seriously consider getting it.
 Excelent textbook for students and reference for researchers January 21, 2008
1 out of 2 found this review helpful
This book is the best one in the field among those I have read and there were quite many. Still, it's not for everyone. If you are not proficient enough in calculus, linear algebra and statistics, you probably should not start reading it. If you are, it will be sheer pleasure to read this book through.
review January 20, 2008
1 out of 3 found this review helpful
Clear explanation!
It has theoretical problems with solutions, which is very good.
Would be nice to have practical problems as well, to make even more clear.
The NIPS view of the (Machine Learning) world December 29, 2007
1 out of 2 found this review helpful
This book is quite good in the material it covers. However, other aspects, for example, decision trees, are only briefly covered. I think this is because this book provides a NIPS (Neural Information Processing Systems conference) view of the world, where only certain aspects of the Machine Learning world are accepted.
This book, together with Duda, Hart, and Stork's book Pattern Classification (2nd Edition) make an excellent pair.
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