Mathematics for Machine Learning

Main
Mathematics - Applied Mathematics
Mathematics for Machine Learning

Mathematics for Machine Learning

Name: Mathematics for Machine Learning
ISBN: 9781108470049

Marc Peter Deisenroth, A Aldo Faisal, Cheng Soon Ong

5.0 / 5.0

4 comments

How much do you like this book?

What’s the quality of the file?

Download the book for quality assessment

What’s the quality of the downloaded files?

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.

Categories:

Mathematics - Applied Mathematics

Content Type:

Books

Year:

2020

Publisher:

Cambridge University Press

Language:

english

ISBN 10:

1108679935