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Linear algebra in data science

Record Type:	Electronic resources : Monograph/item
Title/Author:	Linear algebra in data scienceby 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$q(electronic bk.)

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$q(electronic bk.)

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

183087
Algebras, Linear.

LC Class. No.: QA184.2

Dewey Class. No.: 512.5

Linear algebra in data science
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245 1 0 $a Linear algebra in data science $h [electronic resource] / $c by Peter Zizler, Roberta La Haye.
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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.
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