Understanding machine learning : from theory to algorithms / Shai Shalev-Shwartz, and Shai Ben-David,

By:

Shalev-Shwartz, Shai [Author]

Contributor(s):

Ben-David, Shai [Author]

Material type: Text

TextLanguage: English Publication details: Cambridge : Cambridge University Press, 2022 .Description: xvi, 397 pages ; 26 cm. pbISBN:

9781107512825

Subject(s):

DDC classification:

006.31 SHA

Contents:

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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Item type	Current library	Collection	Call number	Status	Barcode
Book	Ranganathan Library	Computer Science and Technology	006.31 SHA (Browse shelf(Opens below))	Available	048654
Book	Ranganathan Library		006.31 SHA (Browse shelf(Opens below))	Available	046281

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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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