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Understanding machine learning : (Record no. 4552)

MARC details
000 -LEADER
fixed length control field	03277nam a2200301 4500
001 - CONTROL NUMBER
control field	016708076
003 - CONTROL NUMBER IDENTIFIER
control field	BD-ChPU
005 - DATE AND TIME OF LATEST TRANSACTION
control field	20161109111816.0
008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION
fixed length control field	140304s2014 nyua b 001 0 eng
020 ## - INTERNATIONAL STANDARD BOOK NUMBER
International Standard Book Number	9781107057135
040 ## - CATALOGING SOURCE
Original cataloging agency	BD-ChPU
Transcribing agency	BD-ChPU
Modifying agency	BD-ChPU
Language of cataloging	eng
082 ## - DEWEY DECIMAL CLASSIFICATION NUMBER
Classification number	006.31 S528u 2014
Edition number	22
100 ## - MAIN ENTRY--PERSONAL NAME
Personal name	Shalev-Shwartz, Shai.
245 ## - TITLE STATEMENT
Title	Understanding machine learning :
Remainder of title	from theory to algorithms /
Statement of responsibility, etc	Shai Shalev-Shwartz, Shai Ben-David.
250 ## - EDITION STATEMENT
Edition statement	First edition.
260 ## - PUBLICATION, DISTRIBUTION, ETC. (IMPRINT)
Place of publication, distribution, etc	New York :
Name of publisher, distributor, etc	Cambridge University Press,
Date of publication, distribution, etc	2014.
--	2016.[Reprinted]
300 ## - PHYSICAL DESCRIPTION
Extent	xvii, 449 pages :
Other physical details	illustrations ;
Dimensions	26 cm.
500 ## - GENERAL NOTE
General note	Formerly CIP.
504 ## - BIBLIOGRAPHY, ETC. NOTE
Bibliography, etc	Includes bibliographical references and index.
505 ## - 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 an extensive theoretical account of the fundamental ideas underlying machine learning and the mathematical derivations that transform these principles into practical algorithms. Following a presentation of the basics of the field, the book covers a wide array of central topics that have not been addressed 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 an advanced undergraduate or beginning graduate course, the text makes the fundamentals and algorithms of machine learning accessible to students and non-expert readers in statistics, computer science, mathematics, and engineering"-- \|c Provided by publisher.
526 ## - STUDY PROGRAM INFORMATION NOTE
Program name	Computer Science and Engineering.
650 ## - SUBJECT ADDED ENTRY--TOPICAL TERM
Topical term or geographic name as entry element	Machine learning.
9 (RLIN)	3635
650 ## - SUBJECT ADDED ENTRY--TOPICAL TERM
Topical term or geographic name as entry element	Algorithms.
650 ## - SUBJECT ADDED ENTRY--TOPICAL TERM
Topical term or geographic name as entry element	COMPUTERS / Computer Vision & Pattern Recognition.
Source of heading or term	bisacsh
700 ## - ADDED ENTRY--PERSONAL NAME
Personal name	Ben-David, Shai.
942 ## - ADDED ENTRY ELEMENTS (KOHA)
Source of classification or shelving scheme	Dewey Decimal Classification
Koha item type	Books

Holdings
Source of classification or shelving scheme	Home library	Current library	Date acquired	Source of acquisition	Total Checkouts	Full call number	Barcode	Date last seen	Copy number	Price effective from	Koha item type	Date checked out
Dewey Decimal Classification	Premier University Faculty of Engineering Library	Premier University Central Library	29/08/2016	purchase	2	006.31 S528u 2014	17972	07/05/2022	2	02/11/2016	Books	08/11/2021
Dewey Decimal Classification	Premier University Faculty of Engineering Library	Premier University Faculty of Engineering Library	29/08/2016	Purchase		006.31 S528u 2014	17971	04/09/2016	1	04/09/2016	Books
Dewey Decimal Classification	Premier University Faculty of Engineering Library	Premier University Faculty of Engineering Library	29/08/2016	purchase		006.31 S528u 2014	17973	02/11/2016	3	02/11/2016	Books