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Understanding Machine Learning: From Theory to Algorithms/ Shai Shalev-Shwartz, Shai Ben-David

By: Shalev-Shwartz, Shai Contributor(s): Ben-David, Shai Publication details: New Delhi: Cambridge University Press, 2014Edition: 1st edDescription: xvi, 397 p. : ill. ; 26 cmISBN: 9781107512825Subject(s): Machine Learning | Algorithms | Computers

Contents:

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 Nonuniform 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 -- 23Dimensionality 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.

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Holdings
Item type	Current library	Call number	Status	Date due	Barcode	Item holds
General Books	Central Library, Sikkim University	006.31 SHA/U (Browse shelf(Opens below))	Available		49449

Total holds: 0

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 Nonuniform 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 --
23Dimensionality 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.

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