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Mathematics for machine learning

Deisenroth, Marc Peter.

Mathematics for machine learning

紀錄類型:	書目-語言資料,印刷品 : 單行本
作者:	DeisenrothMarc Peter.,
其他作者:	FaisalA. Aldo.,
其他作者:	OngCheng Soon.,
出版地:	Cambridge, United Kingdom
出版者:	Cambridge University Press;
出版年:	2020.
面頁冊數:	xvii, 371 p.ill. (chiefly col.) : 26 cm.;
標題:	Machine learning - Mathematics. -
摘要註:	"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."--Provided by publisher.
ISBN:	9781108455145
內容註:	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.

Mathematics for machine learning
Deisenroth, Marc Peter.

Mathematics for machine learning / Marc Peter Deisenroth, A. Aldo Faisal, Cheng Soon Ong. - Cambridge, United Kingdom : Cambridge University Press, 2020.. - xvii, 371 p. ; ill. (chiefly col.) ; 26 cm..
Introduction and motivation.
Includes bibliographical references (p. 357-366) and index..
ISBN 9781108455145ISBN 110845514XISBN 9781108470049ISBN 1108470041
Machine learning -- Mathematics.

Faisal, A. Aldo.

Mathematics for machine learning
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200 1 $a Mathematics for machine learning $f Marc Peter Deisenroth, A. Aldo Faisal, Cheng Soon Ong.
210 $a Cambridge, United Kingdom $a New York, USA $c Cambridge University Press $d 2020.
215 1 $a xvii, 371 p. $c ill. (chiefly col.) $d 26 cm.
320 $a Includes bibliographical references (p. 357-366) and index.
327 1 $a Introduction and motivation $a Linear algebra $a Analytic geometry $a Matrix decompositions $a Vector calculus $a Probability and distribution $a Continuous optimization $a When models meet data $a Linear regression $a Dimensionality reduction with principal component analysis $a Density estimation with Gaussian mixture models $a Classification with support vector machines.
330 $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."--Provided by publisher.
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