Understanding machine learning : from theory to algorithms

By:

Shalev-Shwartz, Shai

Contributor(s):

Ben-David, Shai

Material type: Text

TextPublication details: New Delhi Cambridge University Press 2014Description: xvi, 397pISBN:

9781107512825 (pbk.)

Subject(s):

DDC classification:

006.31 SHA

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 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

Tags from this library: No tags from this library for this title. Log in to add tags.

Average rating: 0.0 (0 votes)

Holdings
Item type	Current library	Collection	Call number	Status	Date due	Barcode
Book	Plaksha University Library	Computer science	006.31 SHA (Browse shelf(Opens below))	Available		001806

Browsing Plaksha University Library shelves, Collection: Computer science Close shelf browser (Hides shelf browser)

Previous								Next
Previous	006.31 SEJ The deep learning revolution	006.31 SEJ The deep learning revolution	006.31 SEJ The deep learning revolution	006.31 SHA Understanding machine learning : from theory to algorithms	006.31 STA Graph machine learning : take graph data to the next level by applying machine learning techniques and algorithms	006.31 STA Graph machine learning : take graph data to the next level by applying machine learning techniques and algorithms	006.31 SUT Reinforcement learning : an introduction	Next

"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

There are no comments on this title.

to post a comment.