Bayesian Reasoning and Machine Learning

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Cambridge University Press, Feb 2, 2012 - Computers - 697 pages
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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.
 

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Contents

Part I Inference in probabilistic models
1
Part II Learning in probabilistic models
163
Part III Machine learning
303
Part IV Dynamical models
487
Part V Approximate inference
585
Background mathematics
655
References
675
Index
689
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About the author (2012)

David Barber is Reader in Information Processing in the Department of Computer Science, University College London.

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