MARC details
| 000 -LEADER |
|---|
| fixed length control field |
03277nam a2200301 4500 |
| 001 - CONTROL NUMBER |
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| control field |
016708076 |
| 003 - CONTROL NUMBER IDENTIFIER |
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| 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 |