Mathematics for Machine Learning

by Marc Peter Deisenroth

Book Reviews

  • As you start to do serious machine learning research and engineering, mathematics becomes more important. The Mathematics for Machine Learning book teaches the math concepts used in ML. Use it to acquire skills needed to keep advancing your ML knowledge. https://t.co/zSpp67kJSg https://t.co/CiZ9qYFzhaLink to Tweet

About 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.

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