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4 of 4 people found the following review helpful
on 27 June 2013
Although it's expensive book I think it worth the money as it is the "Bible" of Machine Learning and Pattern recognition. However, has a lot of mathematics meaning that a strong mathematical background is necessary. I suggest it especially for PhD candidates in this field.
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9 of 10 people found the following review helpful
For people that are interested in machine learning this is a must. Not dry and colorful, I really enjoyed the small boxes with the biographies of the great mathematicians, very cool idea.

Advices:
1) Mathematical background in advanced calculus and linear algebra is required.
2) Basic background in statistics and probability will make the chapters more comprehensible, but if you don't have it, don't despair. The author gives an overview of the required statistical and probabilistic tools in the first chapters and in the appendices.
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3 of 3 people found the following review helpful
This is an outstanding book. The author did not neglect any aspect for a good explanation and even a undergraduate student can follow the points in the book. Even though, the book covers as much an introductory view as a more advanced math in a considerable detail, in a clean and simple way. I totally recommend this book.
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on 28 May 2015
There are a huge number of machine learning books now available. I own many of them. But I don't think any have had such an impact as Chris Bishop's effort here - I certainly count it as my favourite. The material covered is not exhaustive (although good for 2006), but it's a good springboard to many other advanced texts. (The moniker of ML 'Bible' has apparently been passed to Kevin Murphy's book.) What *is* covered is explained with exceptional clarity with an eye for understanding the intuition as well as the theory.

If you are after a practitioners guide, or a first ML book for self study, this probably isn't ideal. It assumes significant familiarity with multivariate calculus, probability and basic stats (identities, moments, regression, MLE etc.). The pitch is probably early post-graduate level, but with a few stretching parts. If this is your background, I think it's a better first ML book than MacKay (Information Theory ...), Murphy (Machine Learning ...), or Hastie et al. (Elements of Statistical Learning), due to its coherence of topics and consistency of depth. But those books are all excellent in their own ways. However, Barber (Bayesian Reasoning ...) is a good alternative.

Most chapters are fairly self contained, so once you've worked your way through the first couple of chapters, you can skip around as required. A particular highlight for me were the chapters on EM and variational methods (ch 9 & 10); I think you'd be hard pressed to find a better explanation of either of them. Finally, worth pointing out it's unrepentantly Bayesian, and lacking some subtelty which may be grating for seasoned statisticians. Nevertheless, if the above sounds like what you're looking for, this is probably a good choice.
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44 of 53 people found the following review helpful
on 14 June 2007
This new book by Chris Bishop covers most areas of pattern recognition quite exhaustively. The author is an expert, this is evidenced by the excellent insights he gives into the complex math behind the machine learning algorithms. I have worked for quite some time with neural networks and have had coursework in linear algebra, probability and regression analysis, and hence found some of the stuff in the book quite illuminating.

But that said, I must point out that the book is very math heavy. Inspite of my considerable background in the area of neural networks, I still was struggling with the equations. This is certainly not the book that can teach one things from the ground up, and thats why I would give it only 3 stars. I am new to kernels, and I am finding the relevant chapters quite confusing. For those who want to build powerful machine learning solutions to their problems, I am sorry but they will have to look elsewhere. This book cant help you build an application, another serious drawback in my opinion. The intended audience for this book I guess are PhD students/researchers who are working with the math related aspects of machine learning, and not undergraduates or working professionals who want to write machine learning code for their applications/projects.
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2 of 2 people found the following review helpful
on 31 October 2013
a reference in the domain of machine learning ... plus the quality of the paper used, the colors .... everything makes this book a must have if you are interested in machine learning
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13 of 16 people found the following review helpful
on 28 January 2007
As a newbie to pattern recognition I found this book very helpful. It is the clearest book I ever read! Accompanying examples and material are very illuminating. I particularly appreciated the gradual introduction of key concepts, often accompanied with practical examples and stimulating exercises.
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7 of 9 people found the following review helpful
on 24 March 2007
Bishop does an excellent job of helping the reader visualize what is going on in the problems and techniques he describes, emphasizing an intuitive grasp of the issues without sacrificing mathematical rigour. The writing is clear and the production is excellent. Strongly recommended both for anyone serious about getting into machine learning and for those already working in it.
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on 6 May 2014
This is a good in-depth book on pattern recognition. The only problem I have with it is that it can be a challenge to read. I find it easier to understand new mathematical techniques and equations when they come coupled with a good intuitive explanation for people who find it harder to just look at an equation and instantly "get it". I find that this book isn't as good at this as I would like, but it is so full of useful information that it's still a great book.
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on 12 February 2015
Great monography about statistic computation and modern pattern recognition. Timeless book.
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