000			04684cam a2200373 a 4500
001			16931139
005			20240320020002.0
008			110822s2011 enka b 001 0 eng
010			_a 2011035553
020			_a9781107439955
040			_aDLC _cDLC _dDLC
042			_apcc
050	0	0	_aQA267 _b.B347 2011
082	0	0	_a006.31 _223 _bBAR-B
084			_aCOM016000 _2bisacsh
100	1		_aBarber, David,
245	1	0	_aBayesian reasoning and machine learning _cDavid Barber.
260			_aNew Delhi : _bCambridge University Press, _c©2012.
300			_axxiv, 697 p. : _bill. ; _c26 cm.
504			_aIncludes bibliographical references and index.
505	8		_aMachine generated contents note: 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.
520			_a"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"--
520			_a"Vast amounts of data present amajor challenge to all thoseworking in computer science, and its many related fields, who need to process and extract value from such data. Machine learning technology is already used to help with this task in a wide range of industrial applications, including search engines, DNA sequencing, stock market analysis and robot locomotion. As its usage becomes more widespread, no student should be without the skills taught in this book. Designed for final-year undergraduate and graduate students, this gentle introduction is ideally suited to readers without a solid background in linear algebra and calculus. It covers everything from basic reasoning to advanced techniques in machine learning, and rucially enables students to construct their own models for real-world problems by teaching them what lies behind the methods. Numerous examples and exercises are included in the text. Comprehensive resources for students and instructors are available online"--
650		0	_aMachine learning.
650		0	_aBayesian statistical decision theory.
650		7	_aCOMPUTERS / Computer Vision & Pattern Recognition. _2bisacsh
856	4	2	_3Cover image _uhttp://assets.cambridge.org/97805215/18147/cover/9780521518147.jpg
856	4	2	_3Contributor biographical information _uhttp://www.loc.gov/catdir/enhancements/fy1117/2011035553-b.html
856	4	2	_3Publisher description _uhttp://www.loc.gov/catdir/enhancements/fy1117/2011035553-d.html
856	4	1	_3Table of contents only _uhttp://www.loc.gov/catdir/enhancements/fy1117/2011035553-t.html
906			_a7 _bcbc _corignew _d1 _eecip _f20 _gy-gencatlg
942			_2ddc _cBK _082
999			_c10107 _d10107