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Understanding machine learning : from theory to algorithms

Understanding machine learning : from theory to algorithms (Loan 1 times)

Material type
단행본
Personal Author
Shalev-Shwartz, Shai. Ben-David, Shai.
Title Statement
Understanding machine learning : from theory to algorithms / Shai Shalev-Shwartz, The Hebrew University, Jerusalem, Shai Ben-David, University of Waterloo, Canada.
Publication, Distribution, etc
New York, NY, USA :   Cambridge University Press,   c2014.  
Physical Medium
xvi, 397 p. : ill. ; 26 cm.
ISBN
9781107057135 (hardback) 1107057132 (hardback)
요약
"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"--
Content Notes
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.
Bibliography, Etc. Note
Includes bibliographical references (p. 385-393) and index.
Subject Added Entry-Topical Term
Machine learning. Algorithms.
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001 000045956960
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008 181010s2014 nyua b 001 0 eng d
010 ▼a 2014001779
020 ▼a 9781107057135 (hardback)
020 ▼a 1107057132 (hardback)
035 ▼a (KERIS)REF000017964916
040 ▼a DLC ▼b eng ▼c DLC ▼e rda ▼d DLC ▼d 211009
050 0 0 ▼a Q325.5 ▼b .S475 2014
082 0 0 ▼a 006.3/1 ▼2 23
084 ▼a 006.31 ▼2 DDCK
090 ▼a 006.31 ▼b S528u
100 1 ▼a Shalev-Shwartz, Shai.
245 1 0 ▼a Understanding machine learning : ▼b from theory to algorithms / ▼c Shai Shalev-Shwartz, The Hebrew University, Jerusalem, Shai Ben-David, University of Waterloo, Canada.
260 ▼a New York, NY, USA : ▼b Cambridge University Press, ▼c c2014.
300 ▼a xvi, 397 p. : ▼b ill. ; ▼c 26 cm.
504 ▼a Includes bibliographical references (p. 385-393) and index.
505 8 ▼a 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 ▼a "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.
650 0 ▼a Machine learning.
650 0 ▼a Algorithms.
700 1 ▼a Ben-David, Shai.
945 ▼a KLPA

Holdings Information

No. Location Call Number Accession No. Availability Due Date Make a Reservation Service
No. 1 Location Science & Engineering Library/Sci-Info(Stacks2)/ Call Number 006.31 S528u Accession No. 121246195 Availability Available Due Date Make a Reservation Service B M

Contents information

Table of Contents

Introduction -- I. Foundations -- A gentle start -- A formal learning model -- Learning via uniform convergence -- The bias-complexity tradeoff -- The VC-dimension -- Nonuniform learnability -- The runtime of learning -- II. From Theory to Algorithms -- Linear predictors -- Boosting -- Model selection and validation -- Convex learning problems -- Regularization and stability -- Stochastic gradient descent -- Support vector machines -- Kernel methods -- Multiclass, ranking, and complex prediction problems -- Decision trees -- Nearest neighbor -- Neural networks -- III. Additional Learning Models -- Online learning -- Clustering -- Dimensionality reduction -- Generative models -- Feature selection and generation -- IV. Advanced Theory -- Rademacher complexities -- Covering numbers -- Proof of the fundamental theorem of learning theory -- Multiclass learnability -- Compression bounds -- PAC-Bayes.

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