000			03556nam a22002057a 4500
999			_c25400 _d25400
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008			190201b ||||| |||| 00| 0 eng d
020			_a9781107439955
040			_cVITAP
082			_223rd Ed. _a006.31 BAR
100			_910338 _aBarber, David
245			_aBayesian Reasoning and Machine Learning / _cDavid Barber
260			_aNew Delhi _bCambridge University Press _c2012
300			_axxiv, 697p. : ill. ; 24cm
500			_aIt includes Appendix, References and Index pages
521			_aMachine 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. Consistent use of modelling encourages students to see the bigger picture while they develop hands-on experience Full downloadable MATLAB toolbox, including demos, equips students to build their own models Website includes figures from the book, LaTeX code for use in slides, and additional teaching material that enables instructors to easily set exercises and assignments Table of Contents 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. Look Inside Table of Contents (232 KB) Marketing Excerpt (206 KB) Copyright Information Page (86 KB) Front Matter (503 KB) Index (247 KB)
650		0	_910339 _aMachine learning; Bayesian statistical decision theory
856			_uhttps://www.cambridge.org/in/academic/subjects/computer-science/pattern-recognition-and-machine-learning/bayesian-reasoning-and-machine-learning?format=HB&isbn=9780521518147
942			_2ddc _cREF _e23rd Ed. _h006.31 BAR