UID:
almahu_9948234339502882
Format:
1 online resource (vii, 151 pages) :
,
digital, PDF file(s).
ISBN:
9781316882177 (ebook)
Content:
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.
Note:
Title from publisher's bibliographic system (viewed on 28 Sep 2018).
Additional Edition:
Print version: ISBN 9781107184589
Language:
English
URL:
https://doi.org/10.1017/9781316882177
URL:
Volltext
(lizenzpflichtig)
