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(9 reviews)
Author: Visit Amazon's David Barber Page
ISBN : 0521518148
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Format: PDF, EPUB
Free download Free Bayesian Reasoning and Machine Learning Hardcover from with Mediafire Link Download Link
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
Book Description
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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Books with free ebook downloads available Free Bayesian Reasoning and Machine Learning
- Hardcover: 708 pages
- Publisher: Cambridge University Press (March 12, 2012)
- Language: English
- ISBN-10: 0521518148
- ISBN-13: 978-0521518147
- Product Dimensions: 1.6 x 7.3 x 9.6 inches
- Shipping Weight: 3.7 pounds (View shipping rates and policies)
Free Bayesian Reasoning and Machine Learning
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.
If you are scouring for an exploratory text in probabilistic reasoning, basic graph concepts, belief networks, graphical models, statistics for machine learning, learning inference, na?ve Bayes, Markov models and machine learning concepts, look no further. Barber has done a praiseworthy job in describing key concepts in probabilistic modeling and probabilistic aspects of machine learning. Don't let the size of this 700 page, 28 chapter long book intimidate you; it is surprisingly easy to follow and well formatted for the modern day reader.
With excellent follow ups in summary, code and exercises, Dr. David Barber a reader at University college London provides a thorough and contemporary primer in machine learning with Bayesian reasoning. Starting with probabilistic reasoning, author provides a refresher that the standard rules of probability are a consistent, logical way to reason with uncertainty. He proceeds to discuss the basic graph concepts and belief networks explaining how we can reason with certain or uncertain evidence using repeated application of Bayes' rule. Since belief network, a factorization of a distribution into conditional probabilities of variables dependent on parental variables, is a specific case of graphical models, the book leads us into the discipline of representing probability models graphically. Followed by efficient inference in trees and the junction tree, the text elucidates on key stages of moralization, triangularization, potential assignment, and message-passing.
I particularly enjoyed the follow up chapter called statistics for machine learning which uniquely discuss the classical univariate distributions including the exponential, Gamma, Beta, Gaussian and Poisson.