Bayesian reasoning and machine learning / (Record no. 49556)

000 -LEADER
fixed length control field 04794cam a2200385 a 4500
001 - CONTROL NUMBER
control field 16931139
003 - CONTROL NUMBER IDENTIFIER
control field CITU
005 - DATE AND TIME OF LATEST TRANSACTION
control field 20200915004210.0
008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION
fixed length control field 110822s2012 enka b 001 0 eng
010 ## - LIBRARY OF CONGRESS CONTROL NUMBER
LC control number 2011035553
020 ## - INTERNATIONAL STANDARD BOOK NUMBER
International Standard Book Number 9780521518147
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 2012
082 00 - DEWEY DECIMAL CLASSIFICATION NUMBER
Classification number 006.3/1
Edition number 23
084 ## - OTHER CLASSIFICATION NUMBER
Classification number COM016000
Number source bisacsh
100 1# - MAIN ENTRY--PERSONAL NAME
Personal name Barber, David,
Dates associated with a name 1968-
245 10 - TITLE STATEMENT
Title Bayesian reasoning and machine learning /
Statement of responsibility, etc. David Barber.
260 ## - PUBLICATION, DISTRIBUTION, ETC.
Place of publication, distribution, etc. Cambridge ;
-- New York :
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. note 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"--
Assigning source Provided by publisher.
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"--
Assigning source Provided by publisher.
650 #0 - SUBJECT ADDED ENTRY--TOPICAL TERM
Topical term or geographic name entry element Machine learning.
650 #0 - SUBJECT ADDED ENTRY--TOPICAL TERM
Topical term or geographic name entry element Bayesian statistical decision theory.
650 #7 - SUBJECT ADDED ENTRY--TOPICAL TERM
Topical term or geographic name entry element COMPUTERS / Computer Vision & Pattern Recognition.
Source of heading or term bisacsh
856 42 - ELECTRONIC LOCATION AND ACCESS
Materials specified Cover image
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856 42 - ELECTRONIC LOCATION AND ACCESS
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856 42 - ELECTRONIC LOCATION AND ACCESS
Materials specified Publisher description
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856 41 - ELECTRONIC LOCATION AND ACCESS
Materials specified Table of contents only
Uniform Resource Identifier <a href="http://www.loc.gov/catdir/enhancements/fy1117/2011035553-t.html">http://www.loc.gov/catdir/enhancements/fy1117/2011035553-t.html</a>
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942 ## - ADDED ENTRY ELEMENTS (KOHA)
Source of classification or shelving scheme
Koha item type BOOK
Edition 2012
Classification part 006.31
Holdings
Withdrawn status Lost status Source of classification or shelving scheme Damaged status Not for loan Permanent Location Current Location Shelving location Date acquired Source of acquisition Cost, normal purchase price Inventory number Full call number Barcode Date last seen Price effective from Koha item type
          COLLEGE LIBRARY COLLEGE LIBRARY SUBJECT REFERENCE 2012-11-16 ALBASA 6412.35 44002 006.31 B233 2012 CITU-CL-44002 2020-09-15 2020-09-15 BOOK