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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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on 12 May 2017
A must-read for everyone in machine learning. The author added many insights but sometimes, these contents are somewhat distracting.
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on 11 February 2015
This book should be colorful inside, but it is gray colors.
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on 23 September 2016
First, the book itself is excellent - a well-established standard text in the field. This review is not about the book itself.

Rather, the score above reflects the fact that the copy I purchased from East West Academic Books (and have only studied seriously a year after I bought it) is printed in black and white. The book features many colour figures that do not convert well to black and white, so a great deal of important information in the book is lost. From the information that I was given when I made the purchase I had no idea this would be the case.

Obviously, this is not acceptable. Beware of this seller, and Amazon, pull up your socks - we need to be able to trust that we are getting what we pay for - not good enough. Unfortunately I only realised what had happened long after purchase.
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on 8 October 2015
I've recently purchased this book, but it turned out to be black-and-white despite it was not mention in the description. The figures in this book are mostly colorful, so I'm highly disappointed.
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on 28 May 2016
I have bought 7 similar books and
This is THE best book for introduction.
Bishop had a summer school videos on relevant topics on youtube, worth watching over and over again.
Ideas are clear and not too heavy going given the complexity of the topics. very well delivered, concepts well explained, well thought through examples. well formatted too.
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on 17 March 2016
The Springer International Edition (which is cheaper) I believe, has no colour, which distracts a little from the readability. Otherwise this is what i wanted and I'm working my way through it.
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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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on 27 May 2017
I love this book due to the way it challenges me mathematically. For a hard-core maths enthusiast this book is a Godsend. However because it assumes a relatively more advanced level in maths especially linear algebra and calculus of variations, I also find that I have to turn to other resources to find where intermediate steps have been skipped. Another problem with the book is paucity of examples. I think a bigger edition including lots of examples and companion MATLAB code showing implementation of equations given in the book would make this a most complete essential ML text.
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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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