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03556nam a22002057a 4500 |
| 005 - DATE AND TIME OF LATEST TRANSACTION |
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20201205110845.0 |
| 008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION |
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190201b ||||| |||| 00| 0 eng d |
| 020 ## - INTERNATIONAL STANDARD BOOK NUMBER |
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| International Standard Book Number |
9781107439955 |
| 040 ## - CATALOGING SOURCE |
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| Transcribing agency |
VITAP |
| 082 ## - DEWEY DECIMAL CLASSIFICATION NUMBER |
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| Edition number |
23rd Ed. |
| Classification number |
006.31 BAR |
| 100 ## - MAIN ENTRY--PERSONAL NAME |
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| 9 (RLIN) |
10338 |
| Personal name |
Barber, David |
| 245 ## - TITLE STATEMENT |
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| Title |
Bayesian Reasoning and Machine Learning / |
| Statement of responsibility, etc. |
David Barber |
| 260 ## - PUBLICATION, DISTRIBUTION, ETC. |
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| Place of publication, distribution, etc. |
New Delhi |
| Name of publisher, distributor, etc. |
Cambridge University Press |
| Date of publication, distribution, etc. |
2012 |
| 300 ## - PHYSICAL DESCRIPTION |
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| Extent |
xxiv, 697p. : ill. ; 24cm |
| 500 ## - GENERAL NOTE |
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| General note |
It includes Appendix, References and Index pages |
| 521 ## - TARGET AUDIENCE NOTE |
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| Target audience note |
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.<br/><br/> Consistent use of modelling encourages students to see the bigger picture while they develop hands-on experience<br/> Full downloadable MATLAB toolbox, including demos, equips students to build their own models<br/> 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<br/><br/>Table of Contents<br/><br/>Preface<br/>Part I. Inference in Probabilistic Models:<br/>1. Probabilistic reasoning<br/>2. Basic graph concepts<br/>3. Belief networks<br/>4. Graphical models<br/>5. Efficient inference in trees<br/>6. The junction tree algorithm<br/>7. Making decisions<br/>Part II. Learning in Probabilistic Models:<br/>8. Statistics for machine learning<br/>9. Learning as inference<br/>10. Naive Bayes<br/>11. Learning with hidden variables<br/>12. Bayesian model selection<br/>Part III. Machine Learning:<br/>13. Machine learning concepts<br/>14. Nearest neighbour classification<br/>15. Unsupervised linear dimension reduction<br/>16. Supervised linear dimension reduction<br/>17. Linear models<br/>18. Bayesian linear models<br/>19. Gaussian processes<br/>20. Mixture models<br/>21. Latent linear models<br/>22. Latent ability models<br/>Part IV. Dynamical Models:<br/>23. Discrete-state Markov models<br/>24. Continuous-state Markov models<br/>25. Switching linear dynamical systems<br/>26. Distributed computation<br/>Part V. Approximate Inference:<br/>27. Sampling<br/>28. Deterministic approximate inference<br/>Appendix. Background mathematics<br/>Bibliography<br/>Index.<br/><br/><br/>Look Inside<br/><br/> Table of Contents (232 KB)<br/> Marketing Excerpt (206 KB)<br/> Copyright Information Page (86 KB)<br/> Front Matter (503 KB)<br/> Index (247 KB) |
| 650 #0 - SUBJECT ADDED ENTRY--TOPICAL TERM |
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| 9 (RLIN) |
10339 |
| Topical term or geographic name entry element |
Machine learning; Bayesian statistical decision theory |
| 856 ## - ELECTRONIC LOCATION AND ACCESS |
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| Uniform Resource Identifier |
<a href="https://www.cambridge.org/in/academic/subjects/computer-science/pattern-recognition-and-machine-learning/bayesian-reasoning-and-machine-learning?format=HB&isbn=9780521518147">https://www.cambridge.org/in/academic/subjects/computer-science/pattern-recognition-and-machine-learning/bayesian-reasoning-and-machine-learning?format=HB&isbn=9780521518147</a> |
| 942 ## - ADDED ENTRY ELEMENTS (KOHA) |
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| Source of classification or shelving scheme |
Dewey Decimal Classification |
| Koha item type |
Reference Book |
| Edition |
23rd Ed. |
| Classification part |
006.31 BAR |
