Mathematics of machine learning: An introduction

Author(s): Arora, Sanjeev

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DC Field	Value	Language
dc.contributor.author	Arora, Sanjeev	-
dc.date.accessioned	2021-10-08T19:50:52Z	-
dc.date.available	2021-10-08T19:50:52Z	-
dc.date.issued	2018	en_US
dc.identifier.citation	Arora, Sanjeev. "Mathematics of machine learning: An introduction." In Proceedings of the International Congress of Mathematicians (2018): pp. 377-390. doi:10.1142/9789813272880_0017	en_US
dc.identifier.uri	https://www.dropbox.com/s/y59petiffzq63gt/main.pdf?dl=0	-
dc.identifier.uri	http://arks.princeton.edu/ark:/88435/pr1cg32	-
dc.description.abstract	Machine learning is the subfield of computer science concerned with creating machines that can improve from experience and interaction. It relies upon mathematical optimization, statistics, and algorithm design. Rapid empirical success in this field currently outstrips mathematical understanding. This elementary article sketches the basic framework of machine learning and hints at the open mathematical problems in it.	en_US
dc.format.extent	377 - 390	en_US
dc.language.iso	en_US	en_US
dc.relation.ispartof	Proceedings of the International Congress of Mathematicians	en_US
dc.rights	Author's manuscript	en_US
dc.title	Mathematics of machine learning: An introduction	en_US
dc.type	Conference Article	en_US
dc.identifier.doi	10.1142/9789813272880_0017	-
pu.type.symplectic	http://www.symplectic.co.uk/publications/atom-terms/1.0/conference-proceeding	en_US

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