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Mathematical pictures at a data scie...

Foucart, Simon,

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Mathematical pictures at a data science exhibition /

紀錄類型:	書目-語言資料,印刷品 : Monograph/item
正題名/作者:	Mathematical pictures at a data science exhibition // Simon Foucart.
作者:	Foucart, Simon,
出版者:	Cambridge ;Cambridge University Press, : 2022.,
面頁冊數:	xx, 318 p. :ill. ;23 cm.
標題:	Big data - Mathematics. -
ISBN:	9781009001854

Mathematical pictures at a data science exhibition /
Foucart, Simon,

Mathematical pictures at a data science exhibition /Simon Foucart. - Cambridge ;Cambridge University Press,2022. - xx, 318 p. :ill. ;23 cm.

Includes bibliographical references and index.

"In the past few decades, heuristic methods adopted by big techcompanies have complemented existing scientific disciplines to form the newfield of Data Science. This text provides deep and comprehensive coverage ofthe mathematical theory supporting the field. Composed of 27 lecture-lengthchapters with exercises, it embarks the readers on an engaging itinerarythrough key subjects in data science, including machine learning, optimalrecovery, compressive sensing (also known as compressed sensing),optimization, and neural networks. While standard material is covered, thebook also includes distinctive presentations of topics such as reproducing kernel Hilbert spaces, spectral clustering, optimal recovery, compressivesensing, group testing, and applications of semidefinite programming. Students and data scientists with less mathematical background willappreciate the appendices that supply more details on some of the abstract concepts"--

ISBN: 9781009001854GBP29.99

LCCN: 2022002178Subjects--Topical Terms:

2147694
Big data
--Mathematics.

LC Class. No.: QA76.9.B45 / F68 2022

Dewey Class. No.: 005.7

Mathematical pictures at a data science exhibition /
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504 $a Includes bibliographical references and index.
520 # $a "In the past few decades, heuristic methods adopted by big techcompanies have complemented existing scientific disciplines to form the newfield of Data Science. This text provides deep and comprehensive coverage ofthe mathematical theory supporting the field. Composed of 27 lecture-lengthchapters with exercises, it embarks the readers on an engaging itinerarythrough key subjects in data science, including machine learning, optimalrecovery, compressive sensing (also known as compressed sensing),optimization, and neural networks. While standard material is covered, thebook also includes distinctive presentations of topics such as reproducing kernel Hilbert spaces, spectral clustering, optimal recovery, compressivesensing, group testing, and applications of semidefinite programming. Students and data scientists with less mathematical background willappreciate the appendices that supply more details on some of the abstract concepts"-- $c Provided by publisher.
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