Bayesian Reasoning and Machine Learning

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David BarberCambridge University Press, 2/2/2012EAN 9780521518147, ISBN10: 0521518148Hardcover, 735 pages, 25.1 x 19.3 x 3.7 cmLanguage: EnglishMachine learning methods extract val ue 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.PrefacePart I. Inference in Probabilistic Models1. Probabilistic reasoning2. Basic graph concepts3. Belief networks4. Graphical models5. Efficient inference in trees6. The junction tree algorithm7. Making decisionsPart II. Learning in Probabilistic Models8. Statistics for machine learning9. Learning as inference10. Naive Bayes11. Learning with hidden variables12. Bayesian model selectionPart III. Machine Learning13. Machine learning concepts14. Nearest neighbour classification15. Unsupervised linear dimension reduction16. Supervised linear dimension reduction17. Linear models18. Bayesian linear models19. Gaussian processes20. Mixture models21. Latent linear models22. Latent ability modelsPart IV. Dynamical Models23. Discrete-state Markov models24. Continuous-state Markov models25. Switching linear dynamical systems26. Distributed computationPart V. Approximate Inference27. Sampling28. Deterministic approximate inferenceAppendix. Background mathematicsBibliographyIndex.'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, Denmark'With approachable text, examples, exercises, guidelines for teachers, a MATLAB toolbox and an accompanying website, Bayesian Reasoning and Machine Learning by David Barber provides everything needed for your machine learning course. Only students not included.' Jaakko Hollmén, Aalto University'The chapters on graphical models form one of the clearest and most concise presentations I have seen … The exposition throughout 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 a great resource for self-study.' 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 … 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 Show more