東華大學圖書館 |

Language: English

Help

回圖書館首頁

手機版館藏查詢

Back

Switch To: Labeled | MARC Mode | ISBD

Mathematical pictures at a data scie...

Foucart, Simon.

Linked to FindBook

Google Book

Amazon

博客來

Mathematical pictures at a data science exhibition

Record Type:	Electronic resources : Monograph/item
Title/Author:	Mathematical pictures at a data science exhibition/ Simon Foucart.
Author:	Foucart, Simon.
Published:	Cambridge :Cambridge University Press, : 2022.,
Description:	xx, 318 p. :ill., digital ;23 cm.
Notes:	Title from publisher's bibliographic system (viewed on 07 Apr 2022).
Subject:	Big data - Mathematics. -
Online resource:	https://doi.org/10.1017/9781009003933
ISBN:	9781009003933

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: 9781009003933Subjects--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
LDR:01913nmm a2200253 a 4500 001 2324430
003 UkCbUP
005 20220503140214.0
006 m d
007 cr nn 008maaau
008 231215s2022 enk o 1 0 eng d
020 $a 9781009003933 $q (electronic bk.)
020 $a 9781316518885 $q (hardback)
020 $a 9781009001854 $q (paperback)
035 $a CR9781009003933
040 $a UkCbUP $b eng $c UkCbUP $d GP
050 0 0 $a QA76.9.B45 $b F68 2022
082 0 0 $a 005.7 $2 23
090 $a QA76.9.B45 $b F762 2022
100 1 $a Foucart, Simon. $3 3645650
245 1 0 $a Mathematical pictures at a data science exhibition $h [electronic resource] / $c Simon Foucart.
260 $a Cambridge : $b Cambridge University Press, $c 2022.
300 $a xx, 318 p. : $b ill., digital ; $c 23 cm.
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.
650 0 $a Big data $x Mathematics. $3 2147694
650 0 $a Information science $x Mathematics. $3 3565421
650 0 $a Computer science $x Mathematics. $3 532725
856 4 0 $u https://doi.org/10.1017/9781009003933