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Linear algebra for data science, machine learning, and signal processing /

Record Type:	Language materials, printed : Monograph/item
Title/Author:	Linear algebra for data science, machine learning, and signal processing /Jeffrey A. Fessler, Raj Rao Nadakuditi.
Author:	Fessler, Jeffrey A.,
other author:	Nadakuditi, Raj Rao,
Published:	New York, NY :Cambridge University Press,2024.
Description:	xviii, 431 p. :col. ill. ;25 cm.
Subject:	Algebras, LinearPopular works.
ISBN:	9781009418140 :

Linear algebra for data science, machine learning, and signal processing /
Fessler, Jeffrey A.,1964-

Linear algebra for data science, machine learning, and signal processing /Jeffrey A. Fessler, Raj Rao Nadakuditi. - New York, NY :Cambridge University Press,2024. - xviii, 431 p. :col. ill. ;25 cm.

Includes bibliographical references (p. [405]-422) and index.

"Master basic matrix methods by seeing how the math is used in practice in a range of data-driven applications. Includes a wealth of engaging exercises for quizzes, self-study and interactive learning, as well as online JULIA demos offering a hands-on learning experience for upper-level undergraduates and first-year graduate students"--

ISBN: 9781009418140 :NT$1807

LCCN: 2023050878Subjects--Topical Terms:

397301
Algebras, Linear
--Popular works.

LC Class. No.: QA184.2 / .F47 2024

Dewey Class. No.: 512/.5

Linear algebra for data science, machine learning, and signal processing /
LDR:01322cam a2200241 a 4500 001 657822
003 OCoLC
005 20240912040940.0
008 240920s2024 nyua b 001 0 eng
010 $a 2023050878
020 $a 9781009418140 : $c NT$1807
020 $z 9781009418164 (epub)
035 $a (OCoLC)1409608593
035 $a on1409608593
040 $a DLC $b eng $c DLC $d OCLCO $d OCLCF $d OCLCO
042 $a pcc
050 0 0 $a QA184.2 $b .F47 2024
082 0 0 $a 512/.5 $2 23
100 1 $a Fessler, Jeffrey A., $d 1964- $e author. $3 968854
245 1 0 $a Linear algebra for data science, machine learning, and signal processing / $c Jeffrey A. Fessler, Raj Rao Nadakuditi.
260 $a New York, NY : $b Cambridge University Press, $c 2024.
300 $a xviii, 431 p. : $b col. ill. ; $c 25 cm.
504 $a Includes bibliographical references (p. [405]-422) and index.
520 $a "Master basic matrix methods by seeing how the math is used in practice in a range of data-driven applications. Includes a wealth of engaging exercises for quizzes, self-study and interactive learning, as well as online JULIA demos offering a hands-on learning experience for upper-level undergraduates and first-year graduate students"-- $c Provided by publisher.
650 0 $a Algebras, Linear $v Popular works. $3 397301
650 0 $a Matrices $v Textbooks. $3 439902
650 0 $a Machine learning $x Mathematics $v Textbooks. $3 880351
650 0 $a Signal processing $x Mathematics $v Congresses. $3 515700
650 0 $a Julia (Computer program language) $3 797688
700 1 $a Nadakuditi, Raj Rao, $d 1977- $e author. $3 968855
776 0 8 $i Online version: $a A. Fessler, Jeffrey, 1964- $t Linear algebra for data science, machine learning, and signal processing $d New York : Cambridge University Press, 2024 $z 9781009418164 $w (DLC) 2023050879