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Linear algebra in data science

Record Type:	Electronic resources : Monograph/item
Title/Author:	Linear algebra in data science/ by Peter Zizler, Roberta La Haye.
Author:	Zizler, Peter.
other author:	La Haye, Roberta.
Published:	Cham :Springer International Publishing : : 2024.,
Description:	viii, 199 p. :ill., digital ;24 cm.
Contained By:	Springer Nature eBook
Subject:	Algebras, Linear. -
Online resource:	https://doi.org/10.1007/978-3-031-54908-3
ISBN:	9783031549083

Linear algebra in data science
Zizler, Peter.

Linear algebra in data science[electronic resource] /by Peter Zizler, Roberta La Haye. - Cham :Springer International Publishing :2024. - viii, 199 p. :ill., digital ;24 cm. - Compact textbooks in mathematics,2296-455X. - Compact textbooks in mathematics..

This textbook explores applications of linear algebra in data science at an introductory level, showing readers how the two are deeply connected. The authors accomplish this by offering exercises that escalate in complexity, many of which incorporate MATLAB. Practice projects appear as well for students to better understand the real-world applications of the material covered in a standard linear algebra course. Some topics covered include singular value decomposition, convolution, frequency filtering, and neural networks. Linear Algebra in Data Science is suitable as a supplement to a standard linear algebra course.

ISBN: 9783031549083

Standard No.: 10.1007/978-3-031-54908-3doiSubjects--Topical Terms:

521915
Algebras, Linear.

LC Class. No.: QA184.2

Dewey Class. No.: 512.5

Linear algebra in data science
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100 1 $a Zizler, Peter. $3 2153474
245 1 0 $a Linear algebra in data science $h [electronic resource] / $c by Peter Zizler, Roberta La Haye.
260 $a Cham : $b Springer International Publishing : $b Imprint: Birkhäuser, $c 2024.
300 $a viii, 199 p. : $b ill., digital ; $c 24 cm.
490 1 $a Compact textbooks in mathematics, $x 2296-455X
520 $a This textbook explores applications of linear algebra in data science at an introductory level, showing readers how the two are deeply connected. The authors accomplish this by offering exercises that escalate in complexity, many of which incorporate MATLAB. Practice projects appear as well for students to better understand the real-world applications of the material covered in a standard linear algebra course. Some topics covered include singular value decomposition, convolution, frequency filtering, and neural networks. Linear Algebra in Data Science is suitable as a supplement to a standard linear algebra course.
650 0 $a Algebras, Linear. $3 521915
650 1 4 $a Linear Algebra. $3 3338825
650 2 4 $a Data Science. $3 3538937
650 2 4 $a Mathematical Applications in Computer Science. $3 1567978
700 1 $a La Haye, Roberta. $3 3717926
710 2 $a SpringerLink (Online service) $3 836513
773 0 $t Springer Nature eBook
830 0 $a Compact textbooks in mathematics. $3 2107015
856 4 0 $u https://doi.org/10.1007/978-3-031-54908-3
950 $a Mathematics and Statistics (SpringerNature-11649)