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 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.

ISBN:	9781108455145
Publisher:	Cambridge University Press
Imprint:	Cambridge University Press
Published date:	23 Apr 2020
DEWEY:	006.310151
DEWEY edition:	23
Language:	English
Sales rank:	8404
Number of pages:	xvi, 371
Weight:	810g
Height:	234mm
Width:	444mm
Spine width:	15mm

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