Ebook Bayesian Reasoning and Machine Learning, by David Barber
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Bayesian Reasoning and Machine Learning, by David Barber
Ebook Bayesian Reasoning and Machine Learning, by David Barber
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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.
- Sales Rank: #464074 in Books
- Published on: 2012-03-12
- Original language: English
- Number of items: 1
- Dimensions: 9.69" h x 1.46" w x 7.44" l, 3.80 pounds
- Binding: Hardcover
- 735 pages
Review
"With approachable text, examples, exercises, guidelines for teachers, a MATLAB toolbox and an accompanying web site, Bayesian Reasoning and Machine Learning by David Barber provides everything needed for your machine learning course. Only students not included."
Jaakko Hollmén, Aalto University
"Barber has done a commendable job in presenting important concepts in probabilistic modeling and probabilistic aspects of machine learning. The chapters on graphical models form one of the clearest and most concise presentations I have seen. The book has wide coverage of probabilistic machine learning, including discrete graphical models, Markov decision processes, latent variable models, Gaussian process, stochastic and deterministic inference, among others. The material is excellent for advanced undergraduate or introductory graduate course in graphical models, or probabilistic machine learning. The exposition throughout the book uses numerous diagrams and examples, and the book comes with an extensive software toolbox - these will be immensely helpful for students and educators. It's also be a great resource for self-study for people with background knowledge in basic probability and linear algebra."
Arindam Banerjee, University of Minnesota
"I repeatedly get unsolicited comments from my students that the contents of this book have been very valuable in developing their understanding of machine learning. This book appeals to readers from many backgrounds, and is driven by examples of machine learning in action. Despite maintaining that level of accessibility, it does not avoid covering areas that are of practical use but often harder to explain. Neither does it shun a proper understanding of why the methods work; each chapter is a pointer to the overall probabilistic framework upon which these machine learning methods depend. My students praise this book because it is both coherent and practical, and because it makes fewer assumptions regarding the reader's statistical knowledge and confidence than many books in the field."
Amos Storkey, University of Edinburgh
"This book is an exciting addition to the literature on machine learning and graphical models. What makes it unique and interesting is that it provides a unified treatment of machine learning and related fields through graphical models, a framework of growing importance and popularity. Another feature of this book lies in its smooth transition from traditional artificial intelligence to modern machine learning. The book is well-written and truly pleasant to read. I believe that it will appeal to students and researchers with or without a solid mathematical background."
Zheng-Hua Tan, Aalborg University
About the Author
David Barber is Reader in Information Processing in the Department of Computer Science, University College London.
Most helpful customer reviews
109 of 112 people found the following review helpful.
Brilliant and accessible
By T. Triche
Don't take my word for it, though; read the book online. For some reason Amazon decided to delete the URL, so just do a search for David Barber and go to his home page at UCL (University College London), where links to a PDF of the book and to recent publications of his can be found.
Barber has done an excellent job of making extremely complex and contemporary ideas accessible to anyone with a reasonable mathematical background, and he puts them in context ("these techniques can be applied to finance, biology, and speech recognition"... para). Read through it and see for yourself. I find this book more accessible than Daphne Koller & Nir Friedman's (also excellent) text, Probabilistic Graphical Models, despite my immense respect for the authors of the latter.
63 of 65 people found the following review helpful.
Extremely Suitable for Self Study
By Let's Compare Options Preptorial
Unlike many (most?) books and courses on machine learning, Barber's outstanding text is very suitable for self study. There are many reasons for this, and high among them is the fact that he carefully explains, with commonsense examples and applications, many of the tougher logical, mathematical and processing foundations of pattern recognition.
For relative beginners, Bayesian techniques began in the 1700s to model how a degree of belief should be modified to account for new evidence. The techniques and formulas were largely discounted and ignored until the modern era of computing, pattern recognition and AI, now machine learning. The formula answers how the probabilities of two events are related when represented inversely, and more broadly, gives a precise mathematical model for the inference process itself (under uncertainty), where deductive reasoning and logic becomes a subset (under certainty, or when values can resolve to 0/1 or true/false, yes/no etc. In "odds" terms (useful in many fields including optimal expected utility functions in decision theory), posterior odds = prior odds * the Bayes Factor.
For context, I'm the lead scientist at IABOK dot org-- we design algorithms for huge data mining problems and applications. This text is our "go to" reference for programmers not up to speed in many of the new pattern recognition algorithms, including those writing new versions. All the most recent relevant models, from a probability standpoint, are represented here, with a clarity that is stunning. My only criticism (a mild one) is that, when applying Barber's examples to Bodies of Knowledge and data mining, he skips Prolog, backward chaining, predicate calculus and other techniques that are the foundation of automated inference systems (systems that extend knowledge bases automatically by checking whether new propositions can be inferred from the KB as consistent, relevant, etc.).
In the next 20 years, algorithms will rule this planet. If you either want to see the future of your grandkids, or participate in it if you're young, this is a MUST HAVE exploration of where what we used to call AI is now headed. There IS plenty of calculus in this volume, so don't mistakenly think it is "simple" -- but if you put the time in, you can "get it" even if you're a bright undergrad level thinker. The author's goal of training new algorithm programmers is laudable and right on point for where pattern recognition is headed.
With this amount of math, how can we star it high for self study? Easy: unlike most "recipe" books that just give bushels of codes or techniques, the authors here give the what, where when and why of both code and math, not just the how, as their goal is independent, creative contributors who can write their OWN algorithms. There are a few minor UK vs US differences in terminology also (event space instead of sample space, for example), but they expand the reader's horizon rather than distract or annoy as some others do. There are others like Bishop and many more that have more recipes, and more compact and difficult math, but you have to either be really good (just show me the recipe) or really bad (I don't know what I'm doing, but can follow this recipe) to benefit from them. This is a happy middle ground that does not disappoint.
30 of 32 people found the following review helpful.
Great effort put in explanations and examples
By Vladislavs Dovgalecs
First I would like to thank authors (and the publishing house) for giving free PDF of the complete book. That's a really kind of them. Knowing its contents, that motivated me to buy a hard copy for my library.
Now to the content. The author has done a great job in introducing probabilistic concepts and pushing forward to more advanced and practically interesting techniques. There are many examples in the text that often help to grasp the workings of a method or an approach.
For me who has very little background in probabilistic methods this is a real textbook. I am still reading it chapter by chapter and can recommend it as a reading for advanced undergraduate, graduate and pHD students. The material in each chapter is well introduced and motivated, equations are just in time with variables and transformations explained, and with numerous exercises at the end of each chapter. All this makes a book almost self-consistent onto at least a semester can be taught.
The book is also accompanied with MATLAB code to which author refers to at the end of each chapter. The code is organized as a toolbox of functions with demos for each chapter. This allows to apply the acquired knowledge on your own problems.
As of the moment of writing, I've found just few typos that were not that disturbing. I don't see any other more serious reason not to give solid 5 stars to this book.
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