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Bayesian reasoning and machine learning (Record no. 10107)

000 -LEADER
fixed length control field 04684cam a2200373 a 4500
001 - CONTROL NUMBER
control field 16931139
005 - DATE AND TIME OF LATEST TRANSACTION
control field 20200116020003.0
008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION
fixed length control field 110822s2011 enka b 001 0 eng
010 ## - LIBRARY OF CONGRESS CONTROL NUMBER
LC control number 2011035553
020 ## - INTERNATIONAL STANDARD BOOK NUMBER
International Standard Book Number 9781107439955
040 ## - CATALOGING SOURCE
Original cataloging agency DLC
Transcribing agency DLC
Modifying agency DLC
042 ## - AUTHENTICATION CODE
Authentication code pcc
050 00 - LIBRARY OF CONGRESS CALL NUMBER
Classification number QA267
Item number .B347 2011
082 00 - DEWEY DECIMAL CLASSIFICATION NUMBER
Classification number 006.31
Edition number 23
Item number BAR-B
084 ## - OTHER CLASSIFICATION NUMBER
Classification number COM016000
Source of number bisacsh
100 1# - MAIN ENTRY--PERSONAL NAME
Personal name Barber, David,
245 10 - TITLE STATEMENT
Title Bayesian reasoning and machine learning
Statement of responsibility, etc David Barber.
260 ## - PUBLICATION, DISTRIBUTION, ETC. (IMPRINT)
Place of publication, distribution, etc New Delhi :
Name of publisher, distributor, etc Cambridge University Press,
Date of publication, distribution, etc ©2012.
300 ## - PHYSICAL DESCRIPTION
Extent xxiv, 697 p. :
Other physical details ill. ;
Dimensions 26 cm.
504 ## - BIBLIOGRAPHY, ETC. NOTE
Bibliography, etc Includes bibliographical references and index.
505 8# - FORMATTED CONTENTS NOTE
Formatted contents note Machine 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 ## - SUMMARY, ETC.
Summary, etc "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 ## - SUMMARY, ETC.
Summary, etc "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 - SUBJECT ADDED ENTRY--TOPICAL TERM
Topical term or geographic name as entry element Machine learning.
650 #0 - SUBJECT ADDED ENTRY--TOPICAL TERM
Topical term or geographic name as entry element Bayesian statistical decision theory.
650 #7 - SUBJECT ADDED ENTRY--TOPICAL TERM
Topical term or geographic name as entry element COMPUTERS / Computer Vision & Pattern Recognition.
Source of heading or term bisacsh
856 42 - ELECTRONIC LOCATION AND ACCESS
Materials specified Cover image
Uniform Resource Identifier http://assets.cambridge.org/97805215/18147/cover/9780521518147.jpg
856 42 - ELECTRONIC LOCATION AND ACCESS
Materials specified Contributor biographical information
Uniform Resource Identifier http://www.loc.gov/catdir/enhancements/fy1117/2011035553-b.html
856 42 - ELECTRONIC LOCATION AND ACCESS
Materials specified Publisher description
Uniform Resource Identifier http://www.loc.gov/catdir/enhancements/fy1117/2011035553-d.html
856 41 - ELECTRONIC LOCATION AND ACCESS
Materials specified Table of contents only
Uniform Resource Identifier http://www.loc.gov/catdir/enhancements/fy1117/2011035553-t.html
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942 ## - ADDED ENTRY ELEMENTS (KOHA)
Source of classification or shelving scheme
Item type Books
Koha issues (borrowed), all copies 50
Holdings
Withdrawn status Lost status Source of classification or shelving scheme Damaged status Not for loan Collection code Permanent location Current location Shelving location Date acquired Cost, normal purchase price Total Checkouts Total Renewals Full call number Barcode Date last seen Date last borrowed Cost, replacement price Price effective from Koha item type Bill No. Bill Date PO No. PO Date Vendor/Supplier Due Date
        Not For Loan Computer Science and Engineering IIITD IIITD Reference 2014-06-13 1121.25 5 4 REF 006.31 BAR-B 004434 2019-12-16 2015-03-10 1495.00 2014-06-13 Reference IN7839/14-15 2014-06-12 IIITD/LIC/BS/2012/02/88 2014-06-05 Shankar's Book Agency Pvt. Ltd.  
          Computer Science and Engineering IIITD IIITD General Stacks 2015-12-16 1151.15 25 23 006.31 BAR-B 005761 2020-01-15 2020-01-15 1495.00 2015-12-16 Highly Demanded Book IN2396 2015-12-09 IIITD/LIC/BS/2015/04/2 2015-12-02 International Book Centre 2020-01-22
          Computer Science and Engineering IIITD IIITD General Stacks 2016-03-09 1151.15 19 15 006.31 BAR-B 006254 2020-01-16 2020-01-09 1495.00 2016-03-09 Highly Demanded Book IN24024 2016-03-09 IIITD/LIC/BS/2015/02/11 2016-02-29 Overseas Press India (P) Limited  
          Computer Science and Engineering IIITD IIITD General Stacks 2017-02-14 1151 7 6 006.31 BAR-B 007315 2020-01-06 2020-01-06 1495.00 2017-02-14 Highly Demanded Book IN13318 2017-02-13 IIITD/LIC/BS/2012/09/44 2017-02-01 Intercontinental book agency 2020-01-28
          Computer Science and Engineering IIITD IIITD General Stacks 2017-02-14 1151 10 22 006.31 BAR-B 007316 2020-01-21 2019-11-22 1495.00 2017-02-14 Highly Demanded Book IN13318 2017-02-13 IIITD/LIC/BS/2012/09/44 2017-02-01 Intercontinental book agency  

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