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Understanding machine learning : (Record no. 81793)

000 -LEADER
fixed length control field 03487nam a22003618i 4500
001 - CONTROL NUMBER
control field CR9781107298019
003 - CONTROL NUMBER IDENTIFIER
control field UkCbUP
005 - DATE AND TIME OF LATEST TRANSACTION
control field 20221102214119.0
006 - FIXED-LENGTH DATA ELEMENTS--ADDITIONAL MATERIAL CHARACTERISTICS
fixed length control field m|||||o||d||||||||
007 - PHYSICAL DESCRIPTION FIXED FIELD--GENERAL INFORMATION
fixed length control field cr||||||||||||
008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION
fixed length control field 130717s2014||||enk o ||1 0|eng|d
020 ## - INTERNATIONAL STANDARD BOOK NUMBER
International Standard Book Number 9781107298019 (ebook)
020 ## - INTERNATIONAL STANDARD BOOK NUMBER
Canceled/invalid ISBN 9781107057135 (hardback)
040 ## - CATALOGING SOURCE
Original cataloging agency UkCbUP
Language of cataloging eng
Description conventions rda
Transcribing agency UkCbUP
050 00 - LIBRARY OF CONGRESS CALL NUMBER
Classification number Q325.5
Item number .S475 2014
082 00 - DEWEY DECIMAL CLASSIFICATION NUMBER
Classification number 006.3/1
Edition number 23
100 1# - MAIN ENTRY--PERSONAL NAME
Personal name Shalev-Shwartz, Shai,
Relator term author.
9 (RLIN) 66904
245 10 - TITLE STATEMENT
Title Understanding machine learning :
Remainder of title from theory to algorithms /
Statement of responsibility, etc. Shai Shalev-Shwartz, The Hebrew University, Jerusalem, Shai Ben-David, University of Waterloo, Canada.
264 #1 - PRODUCTION, PUBLICATION, DISTRIBUTION, MANUFACTURE, AND COPYRIGHT NOTICE
Place of production, publication, distribution, manufacture Cambridge :
Name of producer, publisher, distributor, manufacturer Cambridge University Press,
Date of production, publication, distribution, manufacture, or copyright notice 2014.
300 ## - PHYSICAL DESCRIPTION
Extent 1 online resource (xvi, 397 pages) :
Other physical details digital, PDF file(s).
336 ## - CONTENT TYPE
Content type term text
Content type code txt
Source rdacontent
337 ## - MEDIA TYPE
Media type term computer
Media type code c
Source rdamedia
338 ## - CARRIER TYPE
Carrier type term online resource
Carrier type code cr
Source rdacarrier
500 ## - GENERAL NOTE
General note Title from publisher's bibliographic system (viewed on 05 Oct 2015).
505 8# - FORMATTED CONTENTS NOTE
Formatted contents note Machine generated contents note: 1. Introduction; Part I. Foundations: 2. A gentle start; 3. A formal learning model; 4. Learning via uniform convergence; 5. The bias-complexity tradeoff; 6. The VC-dimension; 7. Non-uniform learnability; 8. The runtime of learning; Part II. From Theory to Algorithms: 9. Linear predictors; 10. Boosting; 11. Model selection and validation; 12. Convex learning problems; 13. Regularization and stability; 14. Stochastic gradient descent; 15. Support vector machines; 16. Kernel methods; 17. Multiclass, ranking, and complex prediction problems; 18. Decision trees; 19. Nearest neighbor; 20. Neural networks; Part III. Additional Learning Models: 21. Online learning; 22. Clustering; 23. Dimensionality reduction; 24. Generative models; 25. Feature selection and generation; Part IV. Advanced Theory: 26. Rademacher complexities; 27. Covering numbers; 28. Proof of the fundamental theorem of learning theory; 29. Multiclass learnability; 30. Compression bounds; 31. PAC-Bayes; Appendix A. Technical lemmas; Appendix B. Measure concentration; Appendix C. Linear algebra.
520 ## - SUMMARY, ETC.
Summary, etc. Machine learning is one of the fastest growing areas of computer science, with far-reaching applications. The aim of this textbook is to introduce machine learning, and the algorithmic paradigms it offers, in a principled way. The book provides a theoretical account of the fundamentals underlying machine learning and the mathematical derivations that transform these principles into practical algorithms. Following a presentation of the basics, the book covers a wide array of central topics unaddressed by previous textbooks. These include a discussion of the computational complexity of learning and the concepts of convexity and stability; important algorithmic paradigms including stochastic gradient descent, neural networks, and structured output learning; and emerging theoretical concepts such as the PAC-Bayes approach and compression-based bounds. Designed for advanced undergraduates or beginning graduates, the text makes the fundamentals and algorithms of machine learning accessible to students and non-expert readers in statistics, computer science, mathematics and engineering.
650 #0 - SUBJECT ADDED ENTRY--TOPICAL TERM
Topical term or geographic name entry element Machine learning.
9 (RLIN) 1831
650 #0 - SUBJECT ADDED ENTRY--TOPICAL TERM
Topical term or geographic name entry element Algorithms.
9 (RLIN) 3390
700 1# - ADDED ENTRY--PERSONAL NAME
Personal name Ben-David, Shai,
Relator term author.
9 (RLIN) 66905
776 08 - ADDITIONAL PHYSICAL FORM ENTRY
Relationship information Print version:
International Standard Book Number 9781107057135
856 40 - ELECTRONIC LOCATION AND ACCESS
Uniform Resource Identifier <a href="https://doi.org/10.1017/CBO9781107298019">https://doi.org/10.1017/CBO9781107298019</a>
942 ## - ADDED ENTRY ELEMENTS (KOHA)
Koha item type eTextbook

No items available.