Mathematics for Machine Learning

Paper Book

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.

Conditions of Use

CC BY-NC-SAThis book is licensed under a Creative Commons License (CC BY-NC-SA). You can download the ebook Mathematics for Machine Learning for free.

Title
Mathematics for Machine Learning
Publisher
Author(s)
, ,
Published
2020-04-23
Edition
1
Format
eBook (pdf, epub, mobi)
Pages
390
Language
English
ISBN-10
110845514X
ISBN-13
9781108455145
License
CC BY-NC-SA
Book Homepage
Free eBook, Errata, Code, Solutions, etc.
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