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Mathematics for Machine Learning

Af: Cheng Soon Ong, Marc Peter Deisenroth, A. Aldo Faisal Engelsk Paperback

Mathematics for Machine Learning

Af: Cheng Soon Ong, Marc Peter Deisenroth, A. Aldo Faisal Engelsk Paperback
Tjek vores konkurrenters priser
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. Every chapter includes worked examples and exercises to test understanding. Programming tutorials are offered on the book''s web site.
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Tjek vores konkurrenters priser
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. Every chapter includes worked examples and exercises to test understanding. Programming tutorials are offered on the book''s web site.
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Produktdetaljer
Sprog: Engelsk
Sider: 398
ISBN-13: 9781108455145
Indbinding: Paperback
Udgave:
ISBN-10: 110845514X
Kategori: Machine learning
Udg. Dato: 23 apr 2020
Længde: 15mm
Bredde: 444mm
Højde: 234mm
Forlag: Cambridge University Press
Oplagsdato: 23 apr 2020
Forfatter(e): Cheng Soon Ong, Marc Peter Deisenroth, A. Aldo Faisal
Forfatter(e) Cheng Soon Ong, Marc Peter Deisenroth, A. Aldo Faisal

Kategori Machine learning

ISBN-13 9781108455145

Sprog Engelsk

Indbinding Paperback

Sider 398

Udgave

Længde 15mm

Bredde 444mm

Højde 234mm

Udg. Dato 23 apr 2020

Oplagsdato 23 apr 2020

Forlag Cambridge University Press