Understanding Machine Learning: From Theory to Algorithms

Understanding Machine Learning: From Theory to Algorithms

This book introduces machine learning and the algorithmic paradigms it offers. Designed for advanced undergraduates or beginning graduates, and accessible to students and non-expert readers in statistics, computer science, mathematics and engineering.

Publication date: 31 Dec 2014

ISBN-10: 1107057132

ISBN-13: n/a

Paperback: n/a

Views: 6,578

Type: N/A

Publisher: Cambridge University Press

License: n/a

Post time: 02 Feb 2016 08:44:33

Understanding Machine Learning: From Theory to Algorithms

Understanding Machine Learning: From Theory to Algorithms This book introduces machine learning and the algorithmic paradigms it offers. Designed for advanced undergraduates or beginning graduates, and accessible to students and non-expert readers in statistics, computer science, mathematics and engineering.
Tag(s): Machine Learning
Publication date: 31 Dec 2014
ISBN-10: 1107057132
ISBN-13: n/a
Paperback: n/a
Views: 6,578
Document Type: N/A
Publisher: Cambridge University Press
License: n/a
Post time: 02 Feb 2016 08:44:33
Excerpts from the About page:
Shai Shalev-Shwartz wrote: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 a theoretical account of the fundamentals underlying machine learning and the mathematical derivations that transform these principles into practical algorithms. Following a presentation of the basics, the book covers a wide array of central topics unaddressed 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 advanced undergraduates or beginning graduates, the text makes the fundamentals and algorithms of machine learning accessible to students and non-expert readers in statistics, computer science, mathematics and engineering.




About The Author(s)


Shai Ben-David is a Professor at the School of Computer Science at the University of Waterloo since August 2004. He has taught at the Technion (Israel Institute of Technology), Australian National University in Canberra, and Cornell University. He received his Ph.D. from the Hebrew University for a thesis in set theory. His research focuses on statistical and computational machine learning.

Shai Ben-David

Shai Ben-David is a Professor at the School of Computer Science at the University of Waterloo since August 2004. He has taught at the Technion (Israel Institute of Technology), Australian National University in Canberra, and Cornell University. He received his Ph.D. from the Hebrew University for a thesis in set theory. His research focuses on statistical and computational machine learning.


Shai Shalev-Shwartz is an associate professor at the School of Computer Science and Engineering at the Hebrew University of Jerusalem, Israel. He is also at Mobileye, working on autonomous driving. He received his PhD from the Hebrew University in 2007, and was a research assistant professor at the Toyota Technological Institute at Chicago until June 2009. 

Shai Shalev-Shwartz

Shai Shalev-Shwartz is an associate professor at the School of Computer Science and Engineering at the Hebrew University of Jerusalem, Israel. He is also at Mobileye, working on autonomous driving. He received his PhD from the Hebrew University in 2007, and was a research assistant professor at the Toyota Technological Institute at Chicago until June 2009. 


Book Categories
Sponsors
Icons8, a free icon pack