Mathematics for Machine Learning

Language: English

Published by Cambridge University Press, GB, 2020

1108470041 / 9781108470049

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-9781108470049

Title
Mathematics for Machine Learning
Author
Cheng Soon Ong, Marc Peter Deisenroth, A. Aldo Faisal
Publisher
Cambridge University Press, GB
Publication year
2020
Condition
New
Binding
Hardback
Language
English
ISBN 10
1108470041
ISBN 13
9781108470049
Item weight
980 grams
Series
Book 34 of 38: Studies in Natural Language Processing

Rarewaves USA

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