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Mathematics for machine learning

Mathematics for machine learning (1회 대출)

자료유형
단행본
개인저자
Deisenroth, Marc Peter, author. Faisal, A. Aldo, author. Ong, Cheng Soon, author.
서명 / 저자사항
Mathematics for machine learning / Marc Peter Deisenroth, A. Aldo Faisal, Cheng Soon Ong.
발행사항
Cambridge, UK ;   New York, NY :   Cambridge University Press,   2020.  
형태사항
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"--
내용주기
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.
서지주기
Includes bibliographical references (p. 357-366) and index.
일반주제명
Machine learning --Mathematics.
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020 ▼a 1108470041 (hardcover)
020 ▼a 9781108455145 (paperback)
020 ▼a 110845514X (paperback)
020 ▼a 9781108679930 (electronic publication)
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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

소장정보

No. 소장처 청구기호 등록번호 도서상태 반납예정일 예약 서비스
No. 1 소장처 과학도서관/Sci-Info(2층서고)/ 청구기호 006.31 D325m 등록번호 121258371 도서상태 대출가능 반납예정일 예약 서비스 B M

컨텐츠정보

목차

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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