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Algorithmic aspects of machine learning

Moitra, Ankur.

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Algorithmic aspects of machine learning

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Algorithmic aspects of machine learning/ Ankur Moitra.
作者:	Moitra, Ankur.
出版者:	Cambridge :Cambridge University Press, : 2018.,
面頁冊數:	vii, 151 p. :ill., digital ;24 cm.
附註:	Title from publisher's bibliographic system (viewed on 28 Sep 2018).
標題:	Machine learning - Mathematics. -
電子資源:	https://doi.org/10.1017/9781316882177
ISBN:	9781316882177

Algorithmic aspects of machine learning
Moitra, Ankur.

Algorithmic aspects of machine learning[electronic resource] /Ankur Moitra. - Cambridge :Cambridge University Press,2018. - vii, 151 p. :ill., digital ;24 cm.

Title from publisher's bibliographic system (viewed on 28 Sep 2018).

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.

ISBN: 9781316882177Subjects--Topical Terms:

3442737
Machine learning
--Mathematics.

LC Class. No.: Q325.5 / .M65 2018

Dewey Class. No.: 006.31015181

Algorithmic aspects of machine learning
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520 $a 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.
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