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Mathematical pictures at a data science exhibition

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Mathematical pictures at a data science exhibitionSimon Foucart.
作者:	Foucart, Simon.
出版者:	Cambridge :Cambridge University Press,2022.
面頁冊數:	xx, 318 p. :ill., digital ;23 cm.
附註:	Title from publisher's bibliographic system (viewed on 07 Apr 2022).
標題:	Big dataMathematics.
電子資源:	https://doi.org/10.1017/9781009003933
ISBN:	9781009003933$q(electronic bk.)

Mathematical pictures at a data science exhibition
Foucart, Simon.

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

Title from publisher's bibliographic system (viewed on 07 Apr 2022).

This text provides deep and comprehensive coverage of the mathematical background for data science, including machine learning, optimal recovery, compressed sensing, optimization, and neural networks. In the past few decades, heuristic methods adopted by big tech companies have complemented existing scientific disciplines to form the new field of Data Science. This text embarks the readers on an engaging itinerary through the theory supporting the field. Altogether, twenty-seven lecture-length chapters with exercises provide all the details necessary for a solid understanding of key topics in data science. While the book covers standard material on machine learning and optimization, it also includes distinctive presentations of topics such as reproducing kernel Hilbert spaces, spectral clustering, optimal recovery, compressed sensing, group testing, and applications of semidefinite programming. Students and data scientists with less mathematical background will appreciate the appendices that provide more background on some of the more abstract concepts.

ISBN: 9781009003933$q(electronic bk.)Subjects--Topical Terms:

721851
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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500 $a Title from publisher's bibliographic system (viewed on 07 Apr 2022).
520 $a This text provides deep and comprehensive coverage of the mathematical background for data science, including machine learning, optimal recovery, compressed sensing, optimization, and neural networks. In the past few decades, heuristic methods adopted by big tech companies have complemented existing scientific disciplines to form the new field of Data Science. This text embarks the readers on an engaging itinerary through the theory supporting the field. Altogether, twenty-seven lecture-length chapters with exercises provide all the details necessary for a solid understanding of key topics in data science. While the book covers standard material on machine learning and optimization, it also includes distinctive presentations of topics such as reproducing kernel Hilbert spaces, spectral clustering, optimal recovery, compressed sensing, group testing, and applications of semidefinite programming. Students and data scientists with less mathematical background will appreciate the appendices that provide more background on some of the more abstract concepts.
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