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# Pattern Recognition and Machine Learning

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Pattern recognition has its origins in engineering, whereas machine learning grew out of computer science. However, these activities can be viewed as two facets of the same field, and together they have undergone substantial development over the past ten years. In particular, Bayesian methods have grown from a specialist niche to become mainstream, while graphical models h
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Hardcover, 738 pages

Published
April 6th 2011
by Springer
(first published 2006)

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Ropey Lemmings: "Pattern Recognition and Machine Learning" by Christopher M. Bishop

As far as I can see Machine Learning is the equivalent of going in to B&Q and being told by the enthusiastic sales rep that the washing machine you are looking at is very popular (and therefore you should buy it too). Through clenched teeth I generally growl "That doesn't mean I think it is the best washing machine." Following the herd is not my bag; the ...more

Mar 09, 2010
Manny
is currently reading it

Dave, who knows about these things, recommended it... I have just ordered a copy.

Jul 31, 2011
Wooi Hen Yap
added it

For beginners who need to understand Bayesian perspective on Machine Learning, I'd would say that's the best so far. The author has make good attempt to explain complicated theories in simplified manner by giving examples/applications. The best part of the book are chapters on graphical models (chapter 8), mixture model EM (chap 9) and approximate inference (chap 10). The reason I didn't give 5 stars because it is too narrow a perspective on Machine Learning (only from Bayesian Perspective) that
...more

2. “Inconsistent difficulty”, too much time spent on simple things and very short time spent on complicated stuff.

3. Lack of techniques demonstration on real world problems.

I would not recommend this book as the first to be introduced to Machine Learning, because it tends to go down rabbit holes of technical calculations, which makes things very concrete, but makes it difficult for the reader to keep track of what problem we're solving and to take a step back. I've found MacKay's Information Theory, Inference and Learning ...more

This being said, I think you might want to use other books in combination with this book as reference to make the process a little bit easier.

In addition, some people have put together code (look for PRMLT on GitHub) in Matlab, ...more

I enjoyed it but I also recommended it many times over to friends who knew far less stats than me and they often were extremely compelled by it (good for teaching).

It is an intro book, just to note.

Jul 15, 2010
DJ
added it

recommended reading on machine learning from Gatsby (the neuroscience group in London, not the fictional Roaring 20s tail-chaser)

Some of the approaches it discusses are also largely out of favor, as they've been supplanted by other technologies. But things sometimes come around again.

Beyond that, though, there's a lot of good fundamentals that haven't changed so much.

As other reviewers note, it is a heavi ...more

Recommended for understanding the Bayesian perspective of Machine Learning algorithms but it doesn't give a comparative analysis with Frequentist approach. Good for learning the (theoretical or ) mathematical aspects of algorithms and their graphical representation. Focus on real world applications missing.

P.S.: Used for teaching Bayesian Statistics and Machine Learning course for graduate students

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