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Acceptable! Minor cover wear. Scuffed edges. Slightly frayed corners. Little to no writing or highlighting! General overall wear. USA edition ISBN 9780262018029 ships prime! Acceptable! Minor cover wear. Scuffed edges. Slightly frayed corners. Little to no writing or highlighting! General overall wear. USA edition ISBN 9780262018029 ships prime! See less

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Product details

Publisher ‏ : ‎ The MIT Press; Illustrated edition (August 24, 2012)
Language ‏ : ‎ English
Hardcover ‏ : ‎ 1104 pages
ISBN-10 ‏ : ‎ 0262018020
ISBN-13 ‏ : ‎ 978-0262018029
Item Weight ‏ : ‎ 2.31 pounds
Dimensions ‏ : ‎ 8.25 x 1.79 x 9.27 inches

About the author

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Kevin P. Murphy

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Kevin Patrick Murphy was born in Ireland, grew up in England (BA from Cambridge),

and went to graduate school in the USA (MEng from U. Penn, PhD from UC Berkeley,

Postdoc at MIT). In 2004, he became a professor of computer science and statistics

at the University of British Columbia in Vancouver, Canada. In 2011, he went to

Google in Mountain View, California for his sabbatical. In 2012, he

converted to a full-time research scientist position at Google. Kevin has

published over 50 papers in refereed conferences and journals related

to machine learning and graphical models. He has recently published

an 1100-page textbook called "Machine Learning: a Probabilistic

Perspective" (MIT Press, 2012).

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4.4 out of 5

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Great book

Reviewed in the United States on June 5, 2018

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I love this book. It's cutting-edge and comprehensive as nothing else on the market and has the right mathematical level (for me, at any rate; I find this to be about right, and Hastie et al to be a bit hard). It makes a great desk reference for day-to-day work.

Speaking of comprehensiveness, did you want to know about a symmetric version of KL divergence? It's there in 2.8.2. How about dynamic latent dirichlet allocation? See 27.4.2. Examples from cognitive science? See his refs to Tenenbaum and Xu's work. How about a reference to the fact that consistent preferences allow one to represent them as utilities? See 5.7.3.2. The discussion on utilities is brief, but I appreciate this sort of connection-making between disciplines (ML and micro-economics, in this case), even if cursory. Please note: I don't mean to suggest that his comprehensiveness is merely a question of making short references, rather that over and above all the deep & detailed material he presents he still finds the time/space to add these little cross-connections that, to me, make the book a lot more valuable.

Minor complaints: I wonder why he didn't give much attention to newer neural network architectures (e.g. lstm). It also seems a bit weird that he seems to think of Bayesianism as the "one true way", and I say this as someone who loves the Bayesian philosophy. These are minor quibbles.

2 people found this helpful

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FFxt

This is certainly the best book on machine learning.

Reviewed in the United States on June 5, 2021

Just compare the section on linear regression of this book to any other book and you will see why it's the bible of machine learning and the most important book to read in this field. Nevertheless, it requires quite a bit background in estimation theory.

world citizen

One of the best books on probabilistic approaches to machine learning

Reviewed in the United States on October 26, 2014

One of the best books on probabilistic approaches to machine learning. I like that the author not only describes various techniques but also takes the time to try to pull out the intuitions behind concepts, compares techniques to help the reader understand what makes a technique special and provides a detailed bibliography so that details can be followed up in the original literature. The book covers a huge swath of material from basic theory of Bayesian reasoning all the way to recent work in deep learning models. I find I learn something new every time I pick it up. The first edition had a lot of errors, but there are published errata and I understand that later editions have been fixed.

Panos Lambrianides

Great book as a supplement

Reviewed in the United States on July 11, 2017

This book is a bit of a mixed bag. It is more current than Bishop's book and on paper more complete, but the writing quality varies from chapter to chapter. I think it is best used as a complement to good lecture notes as it is more encyclopedic in nature. The didactic quality is great in some chapters, especially the early ones, but skimpy in the more advanced topics. A great positive of this book is the accompanying software is readily available online.

On the whole a great book to have on your shelf if you are serious about AI, but not on its own. As far as the typos, they are pretty easy to spot so they were not a concern.

14 people found this helpful

Kelly R. Boswell

Pattern Recognition and Machine Learning (Bishop) is also a great book. I also found some videos made by ...

Reviewed in the United States on July 31, 2017

I'd think of this book as a reference book for the field of Machine Learning. It has a bit of everything in it but it doesn't have enough room to cover everything in depth, although it does do so for a few topics (Especially Gaussian Models in Chapter 4). You'll need supplementary texts such as BRML (Bayesian Reasoning and Machine Learning; Barber) and Information Theory, Inference, and Learning Algorithms; MacKay), for example. Both of these books are freely distributed in electronic format by their authors. Pattern Recognition and Machine Learning (Bishop) is also a great book. I also found some videos made by Varun Chandola for a ML course at the University of Buffalo on YouTube. Since I'm new to the field, the videos really helped me through the first several chapters, which is all you need. Once you understand the fundamentals, you can read the rest of the chapters without much of a problem. It's still a big effort, though.

All in all, it's a great book but you have to realize that if the author tried to cover every topic in depth it would be 5 times as big as it already is. So, naturally, some steps in the math are going to be cut out, left as an exercise to the reader, yadda-yadda. I think it's still a 5-star reference book, though.

9 people found this helpful

AU_NLPer

Partial review about some details

Reviewed in the United States on November 8, 2012

After I read this thread [...], I decide to buy this MLAPP. I am not working closely on machine learning, but I use machine learning techniques frequently in my research. The review is partial, because I only carefully read some independent parts.

When decide to buy this book. I care about and expect to obtain information on 1) the appropriateness of when I choose a machine learning method for a task; 2) the big picture of whole machine learning techniques; 3) the intuition of important techniques explained without jargons, and 4) how are these techniques connected;
Additionally, as an English as a second language (ESL) user, I expect the text is well organised and is written without ambiguity.

From my partial reading (mainly section 16.6 and some random stuff), I would say the book indeed covers many popular techniques (topic models, deep learnings). The author also did a very good job in describing terminologies and giving succinct descriptions about the key part and the application of each method. These are the two features I like most.

However, the downsides are 1) it is too abstract for some method descriptions that I need to leverage external materials (e.g. the original paper) to understand the method. Even though the description in the book covers the most important part, it is not sufficient and I would like to see more details. One example is in equation 16.105, why P_b(X) is not explained?
2) The text is like an oral presentation in a very long tutorial intended to cover an index of techniques. Overall, its readability (text in Section 16.6) dispoints me. I know English is not my native language. But I would still argue for this issue. Previous I read some chapters in Bishop's PRML and the lecture notes of machine learning in coursera, they do better jobs.

17 people found this helpful

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Giancarlo V.

ottimo!

Reviewed in Italy on February 9, 2022

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Sooa

Its not a book you want to read from front to back but a great reference. I use it with other books

Reviewed in Canada on June 29, 2017

Bible of Machine learning .

Its not a book you want to read from front to back but a great reference. I use it with other books.

Edward De Brouwer

Great Reference Book

Reviewed in France on October 13, 2017

MLAPP is a good reference book for machine learning for whom want to have mathematical basis for the derivation of state of the art ml methods.

Rusty

Very detailled and math intense

Reviewed in Germany on August 21, 2017

This is in my eyes a very solid machine learning book. Due to the publication date it is more up to date than TESL by Trevor, Hastie et.al. However, this book is probably not very appealing for a beginner in the field, due to the math heavy descriptions. If you are more than just practitioner in the field and you are looking for a comprehensive compendium of classical machine learning techniques this might be for you. Note that the current hype topic deep learning is only covered briefly as a curious outlook in one of the later chapters.

Maddy

Awesome Book

Reviewed in India on January 6, 2016

Awesome Book. This should really be the First book you read on Machine Learning. The Probabilistic Approach and Heavy Math might tire you at first but if you stay on with the materials and after few revisions, all Algorithms that are prominently used in the Industry today start to make sense readily. Understanding the Math and associating the tuning parameters to the Math allows you to better tune your models.

24 people found this helpful

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Machine Learning: A Probabilistic Perspective (Adaptive Computation and Machine Learning series) Illustrated Edition

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Kevin P. Murphy