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

by Kevin P. Murphy (Author)

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ISBN-13: 978-0262018029

ISBN-10: 0262018020

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A comprehensive introduction to machine learning that uses probabilistic models and inference as a unifying approach.

Today's Web-enabled deluge of electronic data calls for automated methods of data analysis. Machine learning provides these, developing methods that can automatically detect patterns in data and then use the uncovered patterns to predict future data. This textbook offers a comprehensive and self-contained introduction to the field of machine learning, based on a unified, probabilistic approach.

The coverage combines breadth and depth, offering necessary background material on such topics as probability, optimization, and linear algebra as well as discussion of recent developments in the field, including conditional random fields, L1 regularization, and deep learning. The book is written in an informal, accessible style, complete with pseudo-code for the most important algorithms. All topics are copiously illustrated with color images and worked examples drawn from such application domains as biology, text processing, computer vision, and robotics. Rather than providing a cookbook of different heuristic methods, the book stresses a principled model-based approach, often using the language of graphical models to specify models in a concise and intuitive way. Almost all the models described have been implemented in a MATLAB software package—PMTK (probabilistic modeling toolkit)—that is freely available online. The book is suitable for upper-level undergraduates with an introductory-level college math background and beginning graduate students.

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Editorial Reviews

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This comprehensive book should be of great interest to learners and practitioners in the field of machine learning.—British Computer Society—

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This book will be an essential reference for practitioners of modern machine learning. It covers the basic concepts needed to understand the field as whole, and the powerful modern methods that build on those concepts. In Machine Learning, the language of probability and statistics reveals important connections between seemingly disparate algorithms and strategies.Thus, its readers will become articulate in a holistic view of the state-of-the-art and poised to build the next generation of machine learning algorithms.

―David Blei, Princeton University

Publisher ‏ : ‎ The MIT Press; Illustrated edition (August 24, 2012)
Language ‏ : ‎ English
Hardcover ‏ : ‎ 1104 pages
ISBN-10 ‏ : ‎ 0262018020
ISBN-13 ‏ : ‎ 978-0262018029
Reading age ‏ : ‎ 18 years and up
Item Weight ‏ : ‎ 4.2 pounds
Dimensions ‏ : ‎ 8.25 x 1.79 x 9.27 inches

Best Sellers Rank: #245,971 in Books (See Top 100 in Books)
- #56 in Machine Theory (Books)
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296 ratings

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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Math Book Reviewer

Bad for Students, Good for Experts

Reviewed in the United States on December 8, 2015

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In a nutshell, the value of reading Murphy's Machine Learning highly depends on what you expect to get out of it.
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As a graduate student who had read a descent number of papers in the field, I feel very conflicted about this textbook.
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If you expect to teach yourself machine learning from this textbook, this is in my opinion almost surely *not* the textbook to get. (0/5 Stars)
-The content of the textbook is highly disorganized. Future chapters are constantly referenced in the text (as if you have already read them!). Perplexingly, meaningful explanations of concepts are often delayed by multiple chapters. (Ex. BIC is introduced in Ch.6 but a mathematical justification is provided only in Ch. 8 when the mathematical justification could have (and should have) been in Ch. 6).
-A number of topics are merely mentioned (like VC dimension) but not actually discussed at any reasonable length, making some sections of the textbook meaningless.
-I would instead recommend the related (but different) text Introduction to Statistical Learning with Applications in R as it is quite accessible.
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However, if you are an instructor and wish to use this textbook as a supplement to a course or are a researcher then Murphy's Machine Learning is in my opinion could be a worthwhile purchase. (4/5 stars)
-The examples, references and illustrations give the textbook a particularly nice touch. (I particularly enjoyed the example of calculating the posterior probability of user ratings of two different items on Amazon).

In summary, if you are an instructor that wants their students to learn how to read challenging exposition to prepare them for reading research papers in the field or if you wish to use this as a reference, then this is a good choice. Otherwise, pass.

131 people found this helpful

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amazonk

A comprehensive framework for understand ML

Reviewed in the United States on April 27, 2019

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I've had this book for a couple of years now. I was initially turned off by the 'Bayesian' attitude of the author
(full disclosure: I'm a trained frequentist statistician) however with a coupe of years of experience in applied ML
as well as reading other ML/Stat. Learning books, I find that this book provides me with the most cohesive and
comprehensive framework for understanding and using ML algorithms/models. It has definitely become my
'gold standard' for reviewing ML related concepts. I find this author's notation and terminology the most useful
compared to other books. Also the author stresses the separation of the model from the algorithm. "This kind of
modularity, where we distinguish the model from the algorithms, is good pedagogy and good engineering" - the author. I agree.

However, for a newbie to ML, I would not recommend this book as your primary source. It can serve as a
secondary source or reference.

11 people found this helpful

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GGreene

Reviewed in the United States on June 9, 2020

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I'm an economist with many years of experience building & using time-series "scenario" models. This book makes evident that many techniques used in econometric model-building are also used in Machine Learning (ML) & share common technical foundations. Understanding these communalities makes it a lot easier to communicate with non-economist data scientists.

At a practical level, the book provides technical explains & provides solid technical rationale for a lot of modelling tricks I've used in the past, & suggested some very clever new ones.

This book is accessible for anyone with a background in reasonably advanced econometrics, say at level of W. H. Greene Econometrics (8th Ed, 2018).

2 people found this helpful

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Panos Lambrianides

Great book as a supplement

Reviewed in the United States on July 11, 2017

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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.

13 people found this helpful

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David Grant

Reads like a draft

Reviewed in the United States on June 7, 2017

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I'm just wrapping up a machine learning course using this text, and I'm not a fan. The book is in dramatic need of an editor, and the index is bordering on useless. (Neither of the entire chapters on logistic or linear regression are findable via the index.) Frequently I've encountered a reference to an acronym that isn't defined in the current chapter, and isn't in the book's index. And it seems like every time I read a passage from this book I find some kind of spelling, capitalization, grammar or formatting error.

I don't find the book to be very readable or good for initially learning material from, but it does seem to have a pretty complete appraisal of the techniques composing each individual concept in ML, which is useful as a jumping off point for googling.

7 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

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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.

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Kai_S

awesome book

Reviewed in the United Kingdom on November 24, 2017

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1 This is the best ML book I can find, though, not very suitable for a beginner.
There are many typos in the first 3 printings. The 4th (and later) printing is much better. What I bought (11/24/2017) is the 6th printing (the same as the 4th).
2 Please note: The book mainly concentrate on various classic supervised and unsupervised learning methods, and not much on deep neural network (tons of materials online, e.g. youtube or tensorflow’s guide),and reinforcement learning (Sutton’s famous book).
3 The following sentences are what I found on Quora, said by the author (2016): “…the current version is admittedly rather hard for beginners. I am actually in the process of writing the second edition, which will ramp up more slowly, making it more accessible to beginners. (I'm also adding new content on deep learning, reinforcement learning, etc.) But it will take me a while to finish (~2 years?).”Before I bought it, I emailed the author to ask his plan on the 2nd edition. He said: “... I am working on it, but it won't be done till late 2018...”.
4 Last but not least, the book binding is firm, and, beautiful :)

16 people found this helpful

newway

sometimes you need to go back to the details

Reviewed in the United Kingdom on April 6, 2019

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It is a good reference book back to the classic ML methods, especially in today's "import tensorflow as tf" mentality, you tend to forget the ideas and math behind the more classic methods, such as EM and CRF. One should never forget the beauty of them.

2 people found this helpful

Alvaro

My favourite machine learning book

Reviewed in the United Kingdom on August 11, 2018

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The only con, is that this particular edition is a bit to heavy, so you have to read it on a table.

The reference book on modern Machine Learning

Reviewed in the United Kingdom on April 19, 2019

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A must read to anybody who is starting in Machine Learning. Some math prerequisites but worth every minute spent on it.

phong vo

Better than PRML of Bishop in my opinion

Reviewed in the United Kingdom on June 4, 2017

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Clearly written. Better than PRML of Bishop in my opinion. I should have bought it earlier.

2 people found this helpful

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