PDF | Mathematics for Machine Learning

Mathematics for Machine Learning

Format: PDF eTextbooks
ISBN-13: 978-1108455145
ISBN-10: 110845514X
Delivery: Instant Download
Authors: Marc Peter Deisenroth
Publisher: Cambridge University Press

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.