- Hardcover: 735 pages
- Publisher: Cambridge University Press; 1 edition (March 12, 2012)
- Language: English
- ISBN-10: 0521518148
- ISBN-13: 978-0521518147
- Product Dimensions: 7.4 x 1.5 x 9.7 inches
- Shipping Weight: 3.7 pounds (View shipping rates and policies)
- Average Customer Review: 23 customer reviews
- Amazon Best Sellers Rank: #321,968 in Books (See Top 100 in Books)
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Bayesian Reasoning and Machine Learning 1st Edition
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"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
This practical introduction for final-year undergraduate and graduate students is ideally suited to computer scientists without a background in calculus and linear algebra. Numerous examples and exercises are provided. Additional resources available online and in the comprehensive software package include computer code, demos and teaching materials for instructors.
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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.
Now coming to the print. I bought the paperback and this print is bad. I would have been fine with it if the seller had described what I'm getting. It would have been up to me to consider the price-quality trade-off. But not mentioning that in the description is disingenuous. I'm not sure who is to blame - the seller or the publisher. The seller is definitely to blame for selling this copy on Amazon - see pics.
Here's the problem: most of the book is printed in black and white. Even the pages that are supposed to have colored images on them. However, there are clumps of successive pages, with just the colored images from these pages inserted within the binding. It's quite clear that all of the book was printed in black and white, and then the relatively expensive colored printing was done by grouping together images from across the chapters. Sometimes you'd see a colored image from a chapter that is appears after the image!
Look at the attached pics.
1st pic: chapter appears after image. The image is tagged as "24.3", but see the equation number that appears quite a few pages later: "17.1.4".
2nd pic: Image 17.15 appears as in black and white in the chapter, but appears later again in the clump of the color-printed pages.
3rd pic: some images do not have a colored version. In the first frame, you have image 24.10 in the chapter, in the 2nd frame you can see that we gave image 24.10 a miss in the colored set of pages. The third frame shows what the image actually looks like in the book.
4th pic: Is this even legal? There was a black strip covering some print at the back. I removed it by applying some water and this is what I saw.