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 exercises
25.1 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]