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Mathematics for Machine Learning

Mathematics for Machine Learning
Paperback
Edition:
1
Authors:
Marc Peter Deisenroth
A. Aldo Faisal
Cheng Soon Ong
Publisher:
Cambridge University Press
Release Date:
2020
ISBN-10:
110845514X
ISBN-13:
9781108455145
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About Mathematics for Machine Learning

"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…

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