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Mathematics for machine learning /

Deisenroth, Marc Peter,

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Mathematics for machine learning /

Record Type:	Language materials, printed : Monograph/item
Title/Author:	Mathematics for machine learning // Marc Peter Deisenroth, A. Aldo Faisal, Cheng Soon Ong.
Author:	Deisenroth, Marc Peter,
other author:	Faisal, A. Aldo,
Published:	Cambridge ;Cambridge University Press, : 2020.,
Description:	xvii, 371 p. :ill. (some col.) ;26 cm.
[NT 15003449]:	Introduction and motivation -- Linear algebra -- Analytic geometry -- Matrix decompositions -- Vector calculus -- Probability and distribution -- Continuous optimization --When models meet data -- Linear regression -- Dimensionality reduction with principal component analysis-- Density estimation with Gaussian mixture models -- Classification with support vector machines.
Subject:	Machine learning - Mathematics. -
ISBN:	9781108455145

Mathematics for machine learning /
Deisenroth, Marc Peter,

Mathematics for machine learning /Marc Peter Deisenroth, A. Aldo Faisal, Cheng Soon Ong. - Cambridge ;Cambridge University Press,2020. - xvii, 371 p. :ill. (some col.) ;26 cm.

Includes bibliographical references and index.

Introduction and motivation -- Linear algebra -- Analytic geometry -- Matrix decompositions -- Vector calculus -- Probability and distribution -- Continuous optimization --When models meet data -- Linear regression -- Dimensionality reduction with principal component analysis-- Density estimation with Gaussian mixture models -- Classification with support vector machines.

"The fundamental mathematical tools needed to understand machine learning include linear algebra, analytic geometry,matrix decompositions, vector calculus, optimization, probability, and statistics. These topics are traditionally taught in disparate courses, making it hard for data science or computer science students, or professionals, to efficiently learn the mathematics. This self-contained textbook bridges the gap between mathematical and machine learning texts, introducing the mathematical concepts with a minimum of prerequisites. It uses these concepts to derive four central machine learning methods: linear regression, principal component analysis, Gaussian mixture models, and support vector machines. For students and others with a mathematical background, these derivations provide a starting point to machine learning texts. For those learning the mathematicsfor the first time, the methods help build intuition and practical experience with applying mathematical concepts"--

ISBN: 9781108455145GBP39.99

LCCN: 2019040762Subjects--Topical Terms:

3442737
Machine learning
--Mathematics.

LC Class. No.: Q325.5 / .D45

Dewey Class. No.: 006.31

Mathematics for machine learning /
LDR:02163cam a2200241 a 4500 001 2386416
005 20191218174402.0
008 250623s2020 enka b 001 0 eng
010 $a 2019040762
020 $a 9781108455145 $q (pbk.) : $c GBP39.99
020 $a 110845514X $q (pbk.)
020 $a 9781108470049 $q (hbk.)
020 $z 9781108679930 $q (epub)
020 $a 110845514X $q (pbk.)
040 $a LBSOR/DLC $b eng $c DLC $d NCU
042 $a nbic
050 # 4 $a Q325.5 $b .D45 $y 2020
082 0 0 $a 006.31 $2 23
100 1 $a Deisenroth, Marc Peter, $e author $3 3756674
245 1 0 $a Mathematics for machine learning / $c Marc Peter Deisenroth, A. Aldo Faisal, Cheng Soon Ong.
260 # $a Cambridge ; $a United Kingdom : $b Cambridge University Press, $c 2020.
300 $a xvii, 371 p. : $b ill. (some col.) ; $c 26 cm.
504 $a Includes bibliographical references and index.
505 0 # $a Introduction and motivation -- Linear algebra -- Analytic geometry -- Matrix decompositions -- Vector calculus -- Probability and distribution -- Continuous optimization --When models meet data -- Linear regression -- Dimensionality reduction with principal component analysis-- Density estimation with Gaussian mixture models -- Classification with support vector machines.
520 # $a "The fundamental mathematical tools needed to understand machine learning include linear algebra, analytic geometry,matrix decompositions, vector calculus, optimization, probability, and statistics. These topics are traditionally taught in disparate courses, making it hard for data science or computer science students, or professionals, to efficiently learn the mathematics. This self-contained textbook bridges the gap between mathematical and machine learning texts, introducing the mathematical concepts with a minimum of prerequisites. It uses these concepts to derive four central machine learning methods: linear regression, principal component analysis, Gaussian mixture models, and support vector machines. For students and others with a mathematical background, these derivations provide a starting point to machine learning texts. For those learning the mathematicsfor the first time, the methods help build intuition and practical experience with applying mathematical concepts"-- $c Provided by publisher.
650 # 0 $a Machine learning $x Mathematics. $3 3442737
700 1 # $a Faisal, A. Aldo, $e author. $3 3756675
700 1 # $a Ong, Cheng Soon, $e author. $3 3756676