Algorithmic Aspects of Machine Learning (Paperback)

Ankur Moitra

ISBN 10: 1316636003 / ISBN 13: 9781316636008

Published by Cambridge University Press, Cambridge, 2018

Paperback. This book bridges theoretical computer science and machine learning by exploring what the two sides can teach each other. It emphasizes the need for flexible, tractable models that better capture not what makes machine learning hard, but what makes it easy. Theoretical computer scientists will be introduced to important models in machine learning and to the main questions within the field. Machine learning researchers will be introduced to cutting-edge research in an accessible format, and gain familiarity with a modern, algorithmic toolkit, including the method of moments, tensor decompositions and convex programming relaxations. The treatment beyond worst-case analysis is to build a rigorous understanding about the approaches used in practice and to facilitate the discovery of exciting, new ways to solve important long-standing problems. Machine learning is reshaping our everyday life. This book explores the theoretical underpinnings in an accessible way, offering theoretical computer scientists an introduction to important models and problems and offering machine learning researchers a cutting-edge algorithmic toolkit. This item is printed on demand. Shipping may be from our Sydney, NSW warehouse or from our UK or US warehouse, depending on stock availability. Seller Inventory # 9781316636008

Synopsis:

Introduces cutting-edge research on machine learning theory and practice, providing an accessible, modern algorithmic toolkit.

About the Author: Ankur Moitra is the Rockwell International Associate Professor of Mathematics at Massachusetts Institute of Technology. He is a principal investigator in the Computer Science and Artificial Intelligence Lab (CSAIL), a core member of the Theory of Computation Group, Machine Learning@MIT, and the Center for Statistics. The aim of his work is to bridge the gap between theoretical computer science and machine learning by developing algorithms with provable guarantees and foundations for reasoning about their behavior. He is a recipient of a Packard Fellowship, a Sloan Fellowship, an National Science Foundation (NSF) CAREER Award, an NSF Computing and Innovation Fellowship and a Hertz Fellowship.

Bibliographic Details

Title: Algorithmic Aspects of Machine Learning (...
Publisher: Cambridge University Press, Cambridge
Publication Date: 2018
Binding: Paperback
Condition: new