Pattern Recognition and Machine Learning (Information Science and Statistics) |  | Author: Christopher M. Bishop
Publisher: Springer
Category: Book
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Rating:  48 reviews
Media: Hardcover
Edition: 1st ed. 2006. Corr. 2nd printing
Pages: 738
Number Of Items: 1
Shipping Weight (lbs): 4
Dimensions (in): 9.4 x 7.6 x 1.8
ISBN: 0387310738
Dewey Decimal Number: 006.4
EAN: 9780387310732
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Product Description
The dramatic growth in practical applications for machine learning over the last ten years has been accompanied by many important developments in the underlying algorithms and techniques. For example, Bayesian methods have grown from a specialist niche to become mainstream, while graphical models have emerged as a general framework for describing and applying probabilistic techniques. The practical applicability of Bayesian methods has been greatly enhanced by the development of a range of approximate inference algorithms such as variational Bayes and expectation propagation, while new models based on kernels have had a significant impact on both algorithms and applications. This completely new textbook reflects these recent developments while providing a comprehensive introduction to the fields of pattern recognition and machine learning. It is aimed at advanced undergraduates or first-year PhD students, as well as researchers and practitioners. No previous knowledge of pattern recognition or machine learning concepts is assumed. Familiarity with multivariate calculus and basic linear algebra is required, and some experience in the use of probabilities would be helpful though not essential as the book includes a self-contained introduction to basic probability theory. The book is suitable for courses on machine learning, statistics, computer science, signal processing, computer vision, data mining, and bioinformatics. Extensive support is provided for course instructors, including more than 400 exercises, graded according to difficulty. Example solutions for a subset of the exercises are available from the book web site, while solutions for the remainder can be obtained by instructors from the publisher. The book is supported by a great deal of additional material, and the reader is encouraged to visit the book web site for the latest information. Coming soon: *For students, worked solutions to a subset of exercises available on a public web site (for exercises marked "www" in the text) *For instructors, worked solutions to remaining exercises from the Springer web site *Lecture slides to accompany each chapter *Data sets available for download
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| Customer Reviews:
Showing reviews 1-5 of 48
 recommend for non statistics majors May 9, 2007
zhiyi (Los Angeles, CA)
41 out of 49 found this review helpful
I started to read this book after I gave up the book "element of statisitcal learning" which I read about 80 pages. I won't say that the latter book EoSL is bad, but it definitely assumes a much higher math background. Also it doesn't give all the derivations and reasonings, so it may take a long time to understand a single paragraph. The reading is slow and frustrating. I read each chapter twice, but still do not think I did get it in my heart.
By contrast, the book "Pattern Recognition and machine learning" assumes much less math background, and usually gives complete derivation and reasoning, which makes it a pleasure to read. Therefore, if you are not in statistics major (but a CS major with reasonable statistics background), I recommend you to start this book.
Answers to some problems are posted in the author's website (just google the author's name). It is a big plus to me.
New Text on Pattern Recognition/Machine Learning September 15, 2006
Lawrence Rabiner
95 out of 118 found this review helpful
I have been working in the field of signal processing and speech for more
than 40 years at AT&T Bell labs and, more recently, as a professor at
Rutgers University and at the Univ. of California at Santa Barbara where I
teach courses in digital speech processing and speech recognition. I am
extremely impressed with Chris Bishop's "Pattern Recognition and Machine
Learning." The writing style is such that understanding is maximized by the
clarity of thought and examples provided. He did a very nice job with the
Hidden Markov Model material. He is to be congratulated on this excellent
addition to the literature.
If only all textbooks were this well-written January 29, 2007
Scott Davies (Saratoga, CA USA)
22 out of 27 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.
Fantastic text December 26, 2006
Michael Harrison
9 out of 11 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...
Great book January 5, 2007
Ryan Steckel (New York, NY USA)
5 out of 8 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!
Showing reviews 1-5 of 48
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