David Barber

Bayesian Reasoning and Machine Learning (eBook, PDF)

• Format: PDF

Produktbeschreibung

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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Produktdetails

• Verlag: Cambridge University Press
• Erscheinungstermin: 2. Februar 2012
• Englisch
• ISBN-13: 9781139118729
• Artikelnr.: 38280138

Autorenporträt

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

Inhaltsangabe

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.

Rezensionen

'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