 | |  | | | Pattern Recognition and Machine Learning (Information Science and Statistics) | | | | | | | |
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| | 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 |  |
| | Product Details | | Author: | Christopher M. Bishop | | Hardcover: | 738 pages | | Publisher: | Springer | | Publication Date: | October 01, 2007 | | Language: | English | | ISBN: | 0387310738 | | Package Length: | 9.37 inches | | Package Width: | 7.56 inches | | Package Height: | 1.81 inches | | Package Weight: | 4.01 pounds | | Average Customer Rating: |  based on 52 reviews |
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 Good Book! Aug 23, 2010
This is a fairly good reference for freshman graduate students for many machine learning topics. The materials are well presented.
A slightly abstruse gem Jul 31, 2010
First, realize that the author has distilled everything he has to say into probably the fewest number of words possible. He is, however, fairly generous with equations, and his derivations are well-presented and easy to follow.
Initially I thought the book was way too dense, and skipped way too many steps, providing what amounted to a rough sketch of the material. That said, after I read the chapters a few times, and consulted other materials, I came to find that every chapter I read in-depth is actually quite thorough.
Cons: Doesn't repeat anything, which is an interesting pedagogical tactic. No examples, so you're on your own in terms of actually implementing these things.
Pros: Provides a relatively large amount of depth on a large number of topics. Equations are derived enough so you really know what's going on.
Bottom line: Buy it. It's dense, and there are no examples, but it's a great staple book because it provides a robust outline of the theory behind many methods. Sure, sometimes you won't know what he's talking about, but you can look up the stuff he leaves out without much fuss. The stuff he does include, though, is very hard to find elsewhere.
1 of 1 found the following review helpful:
 A very nice theoretical book, but not useful for the practitioner Jul 09, 2010
This books reviews the personal illuminations of the author about the fields of Pattern Recognition and Machine Learning. As such it is quite interesting, but only if you have a deep understanding of the field already and want to see a new view on the field. In an interview on Microsoft's Channel 9 (the author works for Microsoft Research, Cambridge, England), the author mentions that this book provides a unifying view of the field with recent break-throughs. The unification is through Bayesian approach. For those that don't know, Bayesian means lots of math and integrals, lots of computation and "you should believe me, this is the right model, because it's Bayesian". From what I've seen Bayesian methods and graphical models seem to work best for images. In practice one is faced with much more acute problems than finding the right values for the hyper-parameters. Topics like feature selection, data imbalance are not discussed.
My feeling is that this book is not appropriate for practitioners in Machine learning and Pattern Recognition. It does not offer any statistical intuition why methods work and how to reason about problems. It's very math heavy, but in my opinion the more math-heavy a machine learning algorithm, the worst it is in practice. Statistical intuition and under what conditions a method works are absent.
I like the book of Hastie, Tibshirani, Friedman: "The elements of statistical learning" that offers much more intuition(discussion), less useless math (from practical point of view) and more experiments. Friedman has produced one of the best-working machine learning algorithms to-date: gradient-boosted decision trees, by putting together four components proven to work in practice (boosting, decision-trees, bagging, and linear combination of week classifiers). Hastie, Tibshirani, Friedman have took lifetimes to think what works and how models related to each other. Graphical models, kernel methods, neural networks: that stuff is only good if you want to write papers, but not solve problems.
Again, a very nice theoretical book, but not useful for the practitioner. This books should be called "My personal unifying theory of Machine Learning and Pattern Recognition using the Bayesian Approach".
0 of 1 found the following review helpful:
Great text! Apr 27, 2010
Text came in well before I expected it, and it was exactly as the seller described. Would do business again with this seller.
0 of 3 found the following review helpful:
A good reference. Nov 20, 2009
This is a great reference for machine learning. There are many figures and the treatment is clear and comprehensive.
To my knowledge, it is the best treatment available. It's not a perfect treatment, but at this stage in the field's development, there is no such thing. Very good for a 2001 book.
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