Mathematics for Machine Learning

Language: English

Published by Cambridge University Press, GB, 2020

110845514X / 9781108455145

Series: Book 34 of 38 - Studies in Natural Language Processing

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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.

Seller Inventory # LU-9781108455145

Title
Mathematics for Machine Learning
Author
Marc Peter Deisenroth, Cheng Soon Ong, A. Aldo Faisal
Publisher
Cambridge University Press, GB
Publication year
2020
Condition
New
Binding
Paperback
Language
English
ISBN 10
110845514X
ISBN 13
9781108455145
Item weight
800 grams
Series
Book 34 of 38: Studies in Natural Language Processing

Rarewaves USA United

HEBRON, KY, U.S.A.

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