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Title:
Understanding machine learning : from foundations to algorithms / Shai Shalev-Shwartz, The Hebrew University, Jerusalem, Shai Ben-David, University of Waterloo, Canada.
Author:
Shalev-Shwartz, Shai.

Ben-David, Shai.
Publication Information:
New York, NY, USA : Cambridge University Press, 2014.
Call Number:
Q325.5 .S475 2014
Abstract:
"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"-- Provided by publisher.
ISBN:
9781107057135
Physical 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.
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