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Understanding machine learning : from theory to algorithms

Author: Shai Shalev-Shwartz; Shai Ben-David
Publisher: New York, NY, USA : Cambridge University Press, 2014.
Edition/Format:   Print book : EnglishView all editions and formats
Summary:
"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  Read more...
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Document Type: Book
All Authors / Contributors: Shai Shalev-Shwartz; Shai Ben-David
ISBN: 9781107057135 1107057132
OCLC Number: 866619766
Description: xvi, 397 pages : illustrations ; 26 cm
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.
Responsibility: Shai Shalev-Shwartz, the Hebrew University, Jerusalem, Shai Ben-David, University of Waterloo, Canada.
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Abstract:

Introduces machine learning and its algorithmic paradigms, explaining the principles behind automated learning approaches and the considerations underlying their usage.  Read more...

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'This elegant book covers both rigorous theory and practical methods of machine learning. This makes it a rather unique resource, ideal for all those who want to understand how to find structure in Read more...

 
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