Understanding Machine Learning_ From Theory to Algorithms [Shalev-Shwartz & Ben-David 2014-05-19].pdf

(2922 KB) Pobierz

Understanding Machine Learning

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 princi-

pled 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. Fol-

lowing a presentation of the basics of the ﬁeld, the book covers a wide

array of central topics that have not been addressed by previous text-

books. These include a discussion of the computational complexity of

learning and the concepts of convexity and stability; important algorith-

mic 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 stu-

dents and nonexpert readers in statistics, computer science, mathematics,

and engineering.

Shai Shalev-Shwartz is an Associate Professor at the School of Computer

Science and Engineering at The Hebrew University, Israel.

Shai Ben-David is a Professor in the School of Computer Science at the

University of Waterloo, Canada.

UNDERSTANDING

MACHINE LEARNING

From Theory to

Algorithms

Shai Shalev-Shwartz

The Hebrew University, Jerusalem

Shai Ben-David

University of Waterloo, Canada