Pattern Recognition and Machine Learning (Information Science and Statistics)
by Christopher M. Bishop
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Book Summary Information
Author: Christopher M. BishopEdition: Hardcover
Audio: English (Unknown); English (Original Language); English (Published)
Published: 2007-10-01
ISBN: 0387310738
Number of pages: 738
Publisher: Springer
Accessories:
- Multimodal Technologies for Perception of Humans: First International Evaluation Workshop on Classification of Events, Activities and Relationships, ... Vision, Pattern Recognition, and Graphics)
- Pixelization Paradigm: Visual Information Expert Workshop, VIEW 2006, Paris, France, April 24-25, 2006, Revised Selected Papers (Lecture Notes in ... Vision, Pattern Recognition, and Graphics)
Book Reviews of Pattern Recognition and Machine Learning (Information Science and Statistics)
Book Review: Very good text, but with some flaws
Summary: 4 Stars
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.
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Summary of Pattern Recognition and Machine Learning (Information Science and Statistics)
This is the first textbook on pattern recognition to present the Bayesian viewpoint. The book presents approximate inference algorithms that permit fast approximate answers in situations where exact answers are not feasible. It uses graphical models to describe probability distributions when no other books apply graphical models to machine learning. 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.Machine Learning Books
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