Pattern Recognition and Machine Learning (Information Science and Statistics)

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.

I take back my previous negative review (DHL returned without delivering to me for some reason not explained). I received the book today and very happy - exactly as expected - excellent quality!

This is a great book with one of the most clear presentations of several fundamental algorithms. In my experience this is a book I keep coming back to.

It's one of the best if not the best book for theory in machine learning. It's readable and very comprehensible for someone who has a mathematical background.

It's a must get for Machine Learning students. It covers every fundamental concept of ML. However, it is not quite beginner level friendly, meaning you are required to have some understanding of basic probability and linear algebra. I am giving four stars due to the way it's printed. The print paper quality is good and I can confirm it is hardcover but the margin is bit unusual with wide space on the left hand side.

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.

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

A must-read for everyone in machine learning. The author added many insights but sometimes, these contents are somewhat distracting.