Bayesian Reasoning and Machine Learning A practical introduction perfect for final-year undergraduate and graduate students without a solid background in linear algebra and calculus. David Barber (Author) 9780521518147, Cambridge University Press Hardback, published 2 February 2012 735 pages, 287 b/w illus. 1 table 260 exercises25.1 x 19.3 x 3.7 cm, 1.71 kg '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 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. Preface Part I. Inference in Probabilistic Models: 1. Probabilistic reasoning 2. Basic graph concepts 3. Belief networks 4. Graphical models 5. Efficient inference in trees 6. The junction tree algorithm 7. Making decisions Part II. Learning in Probabilistic Models: 8. Statistics for machine learning 9. Learning as inference 10. Naive Bayes 11. Learning with hidden variables 12. Bayesian model selection Part III. Machine Learning: 13. Machine learning concepts 14. Nearest neighbour classification 15. Unsupervised linear dimension reduction 16. Supervised linear dimension reduction 17. Linear models 18. Bayesian linear models 19. Gaussian processes 20. Mixture models 21. Latent linear models 22. Latent ability models Part IV. Dynamical Models: 23. Discrete-state Markov models 24. Continuous-state Markov models 25. Switching linear dynamical systems 26. Distributed computation Part V. Approximate Inference: 27. Sampling 28. Deterministic approximate inference Appendix. Background mathematics Bibliography Index. Subject Areas: Machine learning [UYQM], Probability & statistics [PBT]
