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Mathematics for machine learning

Mathematics for machine learning (Loan 1 times)

Material type
단행본
Personal Author
Deisenroth, Marc Peter, author. Faisal, A. Aldo, author. Ong, Cheng Soon, author.
Title Statement
Mathematics for machine learning / Marc Peter Deisenroth, A. Aldo Faisal, Cheng Soon Ong.
Publication, Distribution, etc
Cambridge, UK ;   New York, NY :   Cambridge University Press,   2020.  
Physical Medium
xvii, 371 p. : ill. (some col.) ; 26 cm.
ISBN
9781108470049 (hardcover) 1108470041 (hardcover) 9781108455145 (paperback) 110845514X (paperback) 9781108679930 (electronic publication)
요약
"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"--
Content Notes
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.
Bibliography, Etc. Note
Includes bibliographical references (p. 357-366) and index.
Subject Added Entry-Topical Term
Machine learning --Mathematics.
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020 ▼a 9781108470049 (hardcover)
020 ▼a 1108470041 (hardcover)
020 ▼a 9781108455145 (paperback)
020 ▼a 110845514X (paperback)
020 ▼a 9781108679930 (electronic publication)
035 ▼a (KERIS)REF000019428058
040 ▼a LBSOR/DLC ▼b eng ▼e rda ▼c DLC ▼d OCLCO ▼d UKMGB ▼d YDX ▼d 211009
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082 0 0 ▼a 006.3/1 ▼2 23
084 ▼a 006.31 ▼2 DDCK
090 ▼a 006.31 ▼b D325m
100 1 ▼a Deisenroth, Marc Peter, ▼e author.
245 1 0 ▼a Mathematics for machine learning / ▼c Marc Peter Deisenroth, A. Aldo Faisal, Cheng Soon Ong.
260 ▼a Cambridge, UK ; ▼a New York, NY : ▼b Cambridge University Press, ▼c 2020.
264 1 ▼a Cambridge, United Kingdom ; ▼a New York, NY : ▼b Cambridge University Press, ▼c 2020.
300 ▼a xvii, 371 p. : ▼b ill. (some col.) ; ▼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 (p. 357-366) 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.
700 1 ▼a Faisal, A. Aldo, ▼e author.
700 1 ▼a Ong, Cheng Soon, ▼e author.
945 ▼a KLPA

Holdings Information

No. Location Call Number Accession No. Availability Due Date Make a Reservation Service
No. 1 Location Science & Engineering Library/Sci-Info(Stacks2)/ Call Number 006.31 D325m Accession No. 121258371 Availability Available Due Date Make a Reservation Service B M

Contents information

Table of Contents

1. Introduction and motivation; 2. Linear algebra; 3. Analytic geometry; 4. Matrix decompositions; 5. Vector calculus; 6. Probability and distribution; 7. Optimization; 8. When models meet data; 9. Linear regression; 10. Dimensionality reduction with principal component analysis; 11. Density estimation with Gaussian mixture models; 12. Classification with support vector machines.

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