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Linear algebra for data science /

紀錄類型:	書目-語言資料,印刷品 : Monograph/item
正題名/作者:	Linear algebra for data science /Moshe Haviv.
作者:	Haviv, Moshe,
出版者:	New Jersey (Hackensack) :World Scientific,c2023.
面頁冊數:	xii, 244 p. :ill. (some col.) ;24 cm.
標題:	Algebras, Linear.
ISBN:	9789811276224 :

Linear algebra for data science /
Haviv, Moshe,

Linear algebra for data science /Moshe Haviv. - New Jersey (Hackensack) :World Scientific,c2023. - xii, 244 p. :ill. (some col.) ;24 cm.

Includes bibliographical references and index.

Vector algebra -- Linear independence and linear subspaces -- Orthonormal bases and the Gram-Schmidt process -- Linear functions -- Matrices and matrix operations -- Invertible matrices and the inverse matrix -- The pseudo-inverse matrix, projections and regression -- Determinants -- Eigensystems and diagonalizability -- Systematic matrices -- Singular value decomposition (SVD) -- Stochastic matrices -- Solutions to exercises.

"An introductory text to linear algebra for undergraduates in data science, statistics, computer sciences, economics and engineering. Presents the essentials in mathematical rigor while also providing intuition behind the results. Discusses the applications of linear algebra to data science along the way"--

ISBN: 9789811276224 :NT$2246

LCCN: 2023012310Subjects--Topical Terms:

183087
Algebras, Linear.

LC Class. No.: QA184.2 / .H385 2023

Dewey Class. No.: 512/.5

Linear algebra for data science /
LDR:01625cam a2200277 a 4500 001 675772
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020 $a 9789811276224 : $c NT$2246
020 $a 9811276226
020 $z 9789811276231 (ebook for institutions)
020 $z 9789811276248 (ebook for individuals)
035 $a (OCoLC)1396815624 $z (OCoLC)1375058862 $z (OCoLC)1457959699
035 $a on1396815624
040 $a LBSOR $b eng $c DLC $d OCLCO
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050 0 0 $a QA184.2 $b .H385 2023
082 0 0 $a 512/.5 $2 23
100 1 $a Haviv, Moshe, $e author. $3 989520
245 1 0 $a Linear algebra for data science / $c Moshe Haviv.
260 $a New Jersey (Hackensack) : $b World Scientific, $c c2023.
300 $a xii, 244 p. : $b ill. (some col.) ; $c 24 cm.
504 $a Includes bibliographical references and index.
505 0 $a Vector algebra -- Linear independence and linear subspaces -- Orthonormal bases and the Gram-Schmidt process -- Linear functions -- Matrices and matrix operations -- Invertible matrices and the inverse matrix -- The pseudo-inverse matrix, projections and regression -- Determinants -- Eigensystems and diagonalizability -- Systematic matrices -- Singular value decomposition (SVD) -- Stochastic matrices -- Solutions to exercises.
520 $a "An introductory text to linear algebra for undergraduates in data science, statistics, computer sciences, economics and engineering. Presents the essentials in mathematical rigor while also providing intuition behind the results. Discusses the applications of linear algebra to data science along the way"-- $c Provided by publisher.
650 0 $a Algebras, Linear. $3 183087
650 0 $a Big data $x Mathematics. $3 721851
650 0 $a Mathematical statistics. $3 181877