Bayesian Reasoning and Machine Learning

by David Barber

Machine learning methods extract value from vast data sets quickly and with modest resources. They are established tools in a wide range of industrial applications, including search engines, DNA sequencing, stock market analysis, and robot locomotion, and their use is spreading rapidly. People who know the methods have their choice of rewarding jobs. This hands-on text opens these opportunities to computer science students with modest mathematical backgrounds. It is designed for final-year undergraduates and master's students with limited background in linear algebra and calculus. Comprehensive and coherent, it develops everything from basic reasoning to advanced techniques within the framework of graphical models. Students learn more than a menu of techniques, they develop analytical and problem-solving skills that equip them for the real world. Numerous examples and exercises, both computer based and theoretical, are included in every chapter. Resources for students and instructors, including a MATLAB toolbox, are available online.

Genres: Mathematics, Artificial Intelligence, Computer Science, Textbooks, Reference, Science, Technology

735 pages, Hardcover

First published January 31, 2012

Reviews

[Robert Muller · Rating 2 out of 5]

Rant on. I'm afraid I found this book largely unusable as a reference for machine learning. It is possibly good as a high-level introduction to the mathematics of certain kinds of machine learning (note SVM isn't discussed, it "doesn't fit well with the Bayesian approach"). I didn't find the math particularly well explained, and that math assumes far more math and prob/stat exposure than the author claims in the front of the book (upper-level undergraduate with just linear algebra, but the book is filled with partial differentiation and tons of assumptions about probability, Bayesian methods and practices, and math modeling conventions). He almost completely ignores the practical aspects of Bayesian analysis (MCMC and so on) in favor of the very old way of doing things with "conjugate" distributions and so on; nobody does things this way anymore, at least not in the machine learning circles I run in. His approach may be a bit too dependent on the EM algorithm, I can't really judge that. The examples are cursory and usually don't include actual calculations or steps showing how things are actually done in practice. There are no answers to exercises. The code is spotty at best and is done in Matlab, placing it solidly in the "academic" machine learning framework rather than a more practical place. Add to that the fact that I got a copy of the book that had been misbound with missing pages, which I had to return for a replacement. Quality issues at all levels, then. Oh well, rant off.

[Yasser Mohammad · Rating 5 out of 5]

this is not a book about machine learning in general (no svm for example) but it is a very readable introduction to.the Bayesian approach

[Michiel · Rating 4 out of 5]

Very good general machine learning/graphical model book, much more accessible than Bishop! I liked the emphasis on missing values of some of the chapters. Would recommend the middle part of the book as a good, but slightly unorthodox introduction to machine learning.

[Old Man · Rating 3 out of 5]

I'll be frank and concise.

So far I've read the 4 first chapters (graphic models), and the amount of typos is annoying. Fortunately, the author keeps a free online corrected version. I'm marvelled at how Cambridge University Press is not ashamed of selling a book this expensive with so many typos. They've had almost 10 years to correct them, and I've just bought a book in 2020 with all the typos, so far.

The language used by the author sometimes is not as precise as I would like, leaving space for dubious interpretation, and then adding to this the occasional error, and one may conclude that our learning experience is less than ideal. The abundance of examples tries to compensate for his imprecise language, and sometimes it breaks the reading flow... however, they very interesting, and some even fun.

The physical quality of the book is remarkable, the paper, the colours... the feeling when I touch it. The font size could be increased a bit, since overall it's not that big to start with, and then the remarks are made with an even smaller font size.

I'll update as I progress on my self-study.

[Marta Fajlhauer · Rating 5 out of 5]

I think that I don't need to introduce the author neither the quality of the writing here. David Barber is very famous and often quoted author in the field.

[Tatiana Rymarenko · Rating 5 out of 5]

great intro to bayesian reasoning

[Nicky · Rating 4 out of 5]

A good but somewhat dense introduction to Bayesian reasoning and machine learning. Some concepts lack intuition, while others are well explained both visually and mathematically.

[Steve · Rating 3 out of 5]

I appreciate that the author made a version of this book freely available online, and despite some omissions, the breadth is impressive, but I found some of the proofs to be incomprehensible, and the overall structure to be not-so-helpful. I'd say that EOSL is better as an all-purpose ML textbook.