國立高雄大學圖資館 |

Language: English

Back

Mathematics for machine learning /

Deisenroth, Marc Peter,

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,
Description:	XVI, [1], 371 pages: ills. ; 26 cm.
Subject:	Machine learningMathematics.
ISBN:	9781108470049

Mathematics for machine learning /
Deisenroth, Marc Peter,

Mathematics for machine learning /Marc Peter Deisenroth, A. Aldo Faisal, Cheng Soon Ong. - XVI, [1], 371 pages: ills. ; 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 mathematics for the first time, the methods help build intuition and practical experience with applying mathematical concepts"--

ISBN: 9781108470049

LCCN: 2019040762Subjects--Topical Terms:

857106
Machine learning
--Mathematics.

LC Class. No.: Q325.5 / .D45 2020

Dewey Class. No.: 006.3/1

Mathematics for machine learning /
LDR:02753nam a2200349 a 4500 001 570469
005 20191218174402.0
008 200824s2020 enk b 001 0 eng
010 $a 2019040762
020 $a 9781108470049 $q (hardback)
020 $a 9781108455145 $q (paperback) $c GBP35.99
020 $z 9781108679930 $q (epub)
026 7 4 $a cam 22003378i 4500
035 $a 21336577
040 $a LBSOR/DLC $b eng $e rda $c DLC
042 $a pcc
050 0 0 $a Q325.5 $b .D45 2020
082 0 0 $a 006.3/1 $2 23
100 1 $a Deisenroth, Marc Peter, $e author. $3 857103
245 1 0 $a Mathematics for machine learning / $c Marc Peter Deisenroth, A. Aldo Faisal, Cheng Soon Ong.
263 $a 1912
264 1 $a Cambridge ; $a New York, NY : $b Cambridge University Press, $c 2020.
300 $a XVI, [1], 371 pages: $b ills. ; $c 26 cm.
336 $a text $b txt $2 rdacontent
337 $a unmediated $b n $2 rdamedia
338 $a volume $b nc $2 rdacarrier
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 mathematics for 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 857106
700 1 $a Faisal, A. Aldo, $e author. $3 857104
700 1 $a Ong, Cheng Soon, $e author. $3 857105
776 0 8 $i Online version: $a Deisenroth, Marc Peter. $t Mathematics for machine learning. $d Cambridge, United Kingdom ; New York : Cambridge University Press, 2020. $z 9781108679930 $w (DLC) 2019040763
906 $a 7 $b cbc $c orignew $d 1 $e ecip $f 20 $g y-gencatlg
925 0 $a acquire $b 1 shelf copy $x policy default
955 $b LBSOR 2019-11-30 $e rl02 2019-12-16 to Dewey $w xm10 2019-12-18
985 $a LBSORCIP $d 2019-12-07