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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
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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:
 Only for those with EXTREMELY strong math backgrounds February 14, 2007
6 out of 11 found this review helpful
I'm currently using this textbook for a class, and I have to say that it is the WORST text book I have ever read. Its explanations are never clear and always cluttered with pointless notation which obfuscates its readability.
For instance, it will constantly explain things like "index x whose range is 1...X" for some complicated equation, and then sort of skim over what is actually going on in the rest of the equation. Just a clue: If I could understand the dense, utterly frustrating, notation-crufty equations you let pass unexplained, it would be IMMEDIATELY OBVIOUS (as it already is) that X was the upper bound on your indexing variable x. In fact, you wouldn't even need to explain that x was an indexing variable: I would be able to tell from its use in your sum notation (as I already am). Use the text to actually EXPLAIN IN ENGLISH the significance of the OBSCURE parts of your notation.
This book focuses on explaining the trivially obvious points of its equations and leaves out CLEAR and STAIGHT-FORWARD explanations for what the processes going on in its notation mean. The only reason I am giving it two stars is because it is obviously a wonderful book for someone who is a graduate-level math student, not a vanilla computer science student (even a fairly math savvy one).
 If only all textbooks were this well-written January 29, 2007
21 out of 24 found this review helpful
I was a big fan of Bishop's earlier "Neural Networks for Pattern Recognition" despite my not being particularly interested in neural networks (as opposed to other aspects of machine learning), and so I was pretty excited when I heard about this book. Reading it has not left me disappointed. Like his earlier book, this text is quite mathematically oriented, and not well-suited for people who aren't comfortable with calculus. However, also like in "NNPR", the writing style here is very clear, and everything past basic calculus and linear algebra is well-explained before it's needed. The appendices alone are a goldmine. (Appendix B is a great "cheat sheet" for commonly used probability distributions; Appendix C has lots of useful matrix properties you may have forgotten or never known; Appendix D quickly explains what you need to know about the calculus of variations; and Appendix E does the same for Lagrange multipliers.) The author also does an excellent job throughout the text of marrying math and intuition without giving either short shrift.
However, note that the material covered is inherently pretty complex, so the book can still be intimidating in parts despite the excellent writing. It's more appropriate for, say, Ph.D. students and professional researchers in statistics or machine learning than people who just want to crank out code for a simple classifier. There is very little pseudocode (although copious MATLAB code will supposedly be made available in a companion book due out in 2008), and the book's overall approach to machine learning is basically hard-core Bayesian statistics. If you are not willing to scratch your head for a while over lots and lots of equations, this may not be the book for you.
On the flip side, people who are already experts in machine learning may be mildly disappointed with the lack of coverage some of their pet topics get. For example, while the chapter on graphical models is excellent as far as it goes, it only mentions the problem of learning graphical model structures (one of my areas of interest) in passing. Reinforcement learning (another personal area of interest) is discussed briefly in the introduction and then written off as beyond the scope of the book.
However, the book is already a fabulous resource as it stands; complaining there's not even more of it would be gauche. The cover may look like goat barf, and there are some innocuous missing words here and there (hey, it's a first edition), but if you're serious about machine learning and not afraid of a little math, you should definitely own this book. I can only imagine how much cooler my own thesis research might have been if this book had been around a few years earlier.
 THIS IS A TEXTBOOK! January 26, 2007
4 out of 55 found this review helpful
I was expecting that 700+ book will be scientific monograph. Disappointment: this is a textbook, American style textbook, with wide margins to make notes, color text, color frames, color pictures explaining what is linear regression, gaussian distribution and such.
Just to be clear, as a textbook, this is very good text, and deserves 5 starts. But I am giving just one because of disappointment. Sending back to Amazon. This is not what I was looking for
Great book January 5, 2007
5 out of 6 found this review helpful
Christopher Bishop has a talent for explaining complex subjects. With a background in Data Mining, I think this book is very well written compared to some of the other top books (Elements of Statistical Learning, Pattern Classification, ...). It does get to some in-depth subjects that are beyond me, but the author does a great job of building up to them. He provides alot of introductory material (a whole chapter on probability). After looking at quite a few papers on EM, I felt the chapter on the subject in this book was great. He is also one of the leaders in Graphical Models (which attracted me to this book), and he does a fantastic job in the GM chapter.
This book covers so much material at just the right level (mostly). Definitely recommended!
Fantastic text December 26, 2006
9 out of 10 found this review helpful
I've read many books on statistical pattern recognition and machine learning, and this is my favorite to date. This book is more focused than AIMA (Artificial Intelligence, A Modern Approach), so it serves a complementary role to this classic text.
The beginning lays a solid foundation on probability, decision theory and information theory. I was most interested in the chapters on Graphical Models, Kernel Methods, and Mixture Models & EM. The chapter on Graphical Models is available for preview on Bishop's site.
In addition to providing an insightful and coherent explanation of these techniques, he also introduces some ideas that were new to me: Relevance Vector Machines (as opposed to Support Vector Machines) and Variational Inference. His references are quite recent, and many are from pending texts and articles (It's funny to be reading the book in 2006 and see a reference from 2007.) Better still, soon he will release an accompanying library of Matlab algorithms.
This is a cutting-edge, well-written book. The writing is clear; this is the same author who wrote the widely adopted text "Neural Networks for Pattern Recognition". 5 stars...
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