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11 of 11 people found the following review helpful
5.0 out of 5 stars A standout text, 23 May 2013
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This review is from: Machine Learning: A Probabilistic Perspective (Adaptive Computation and Machine Learning Series) (Hardcover)
The field now collected beneath the standard of 'Machine Learning' is so vast, and draws from so many different schools of thought, and hence mindsets, notations and assumptions, that it is extremely hard to take your bearings. Even knowing what exists, and how it relates to the rest of what exists, is extremely difficult. The old school statistics guys speak one language, the machine learners another, and the Bayesian chaps yet a third, and so although there are many unifying ideas, these are hard to identify. The primary strength of this book is that it allows the reader to see the connections by providing a unifying framework and notation all the way from basic distributions through standard statistical models to machine learning black-boxes and out to applied algorithms. Many sections end in current academic references, as well as current practical uses thereof. I have wanted such a text for a very long time, and am thrilled to have found it.

Beyond that, the approach that the book takes to maths hits the sweet-spot between the thicket of lemma-lemma-theorem-proof found in 'academic' books, and the hand-wavy elisions found in 'practitioners' book. That is, important proofs are stated and fully worked, within the context of softer discussion of the concepts presented. Finally, having the source code for all images in the books allows you to dive in and really understand by doing. Having this code a gold standard off which to base your own software is fantastic.

I have read the other main books in this area (PGM, ESL, PRML etc) and think this is the most broad, thorough and unified presentation available. It can be used as the foundation for understanding this field.
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4 of 4 people found the following review helpful
5.0 out of 5 stars The perfect book for learning and teaching, 21 Oct 2013
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Kevin Murphy's book covers all aspects of statistical learning theory in depth and breadth, taking the reader from basic concepts all the way to cutting edge problems. It is a very rare thing, indeed, to find a textbook that is nigh on impossible to fault (Matlab vs R is the only minor niggle for me), in terms of content, style and delivery. The theoretical underpinnings are outlined with care and the motivating examples are well chosen. It serves as a great introduction to statistical inference, machine learning, information theory and graphical models. This book has quickly become my standard reference on the topic and the main recommendation for students.
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11 of 12 people found the following review helpful
5.0 out of 5 stars a really impressive book, 16 Nov 2012
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This review is from: Machine Learning: A Probabilistic Perspective (Adaptive Computation and Machine Learning Series) (Hardcover)
This is an excellent textbook on machine learning, covering a number of very important topics. The depth and breadth of coverage of probabilistic approaches to machine learning is impressive. Having Matlab code for all the figures is excellent. I highly recommend this book!
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4.0 out of 5 stars Great reference book, 11 May 2014
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Ozzymandias (Mistworld) - See all my reviews
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This review is from: Machine Learning: A Probabilistic Perspective (Adaptive Computation and Machine Learning Series) (Hardcover)
This is more of a reference book to dip into many topics, it won't make you an expert but it is a good taking off point to study different machine learning fields. I love the Bayesian approach as well
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0 of 2 people found the following review helpful
5.0 out of 5 stars 5 stars, 7 Feb 2014
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This review is from: Machine Learning: A Probabilistic Perspective (Adaptive Computation and Machine Learning Series) (Hardcover)
Excelent quality, not only the book itself but also the state in which it was delivered. A must have for every researcher using machine learning!
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