Shop now Shop now Shop now See more Shop all Amazon Fashion Cloud Drive Photos Shop now Learn More Learn more Shop now Shop Fire Shop Kindle Discover more Shop now

Customer Reviews

4.9 out of 5 stars
13
4.9 out of 5 stars
5 star
12
4 star
1
3 star
0
2 star
0
1 star
0

Your rating(Clear)Rate this item
Share your thoughts with other customers

There was a problem filtering reviews right now. Please try again later.

on 23 May 2013
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.
0Comment| 23 people found this helpful. Was this review helpful to you?YesNoReport abuse
on 21 October 2013
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.
11 comment| 7 people found this helpful. Was this review helpful to you?YesNoReport abuse
on 16 November 2012
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!
0Comment| 14 people found this helpful. Was this review helpful to you?YesNoReport abuse
on 2 June 2015
The most thorough theoretical piece on machine and statistical learning and statistical model development I have ever met. Fine print, elegant paper, and lots of colour illustrations. Requiers some previous math/statistical background.
0Comment| One person found this helpful. Was this review helpful to you?YesNoReport abuse
on 11 May 2014
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
11 comment| 2 people found this helpful. Was this review helpful to you?YesNoReport abuse
on 13 May 2015
This is a great book. Having been exposed to the other two popular textbooks in machine learning, "The Elements of Statistical Learning" and "Pattern recognition and Machine Learning", in university courses, I have to say that Murphy's "Machine Learning" is definitely the best one. It the most comprehensive one, it is better at explaining (because there is more detail) and it is also the most up-to-date one
0Comment|Was this review helpful to you?YesNoReport abuse
on 25 June 2016
Very good book. Covers both breadth and depth. Mathematics is highly advanced and I would recommend learning some statistics before going through the book
0Comment|Was this review helpful to you?YesNoReport abuse
on 22 March 2015
This book covers latest machine learning theory and techniques. I use it everyday for reference book. I 'm a postdoc researcher.
0Comment|Was this review helpful to you?YesNoReport abuse
on 21 June 2015
A very comprehensive compendium of techniques. Every data mining practitioner should have this on his/her shelf.
0Comment|Was this review helpful to you?YesNoReport abuse
on 11 March 2015
Very useful book for students and practitioners interested in state of the art methods of machine learning.
0Comment|Was this review helpful to you?YesNoReport abuse

Sponsored Links

  (What is this?)