Mathematics for Machine Learning
Marc Peter Deisenroth, A. Aldo Faisal, Cheng Soon Ong

✅ 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.