Mathematics for Machine Learning

Distills key concepts from linear algebra, geometry, matrices, calculus, optimization, probability and statistics that are used in machine learning.

Marc Peter Deisenroth (Author), A. Aldo Faisal (Author), Cheng Soon Ong (Author)

9781108455145, Cambridge University Press

Paperback / softback, published 23 April 2020

398 pages, 3 b/w illus. 106 colour illus.
25.2 x 17.7 x 1.8 cm, 0.8 kg

'This is an excellent book on the mathematical foundations of machine learning. The colourful figures and even some equations make the content both engaging and easy to follow. I will definitely be recommending this book to my students.' Hom Nath Gharti, Queen's University

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.

1. Introduction and motivation
2. Linear algebra
3. Analytic geometry
4. Matrix decompositions
5. Vector calculus
6. Probability and distribution
7. Optimization
8. When models meet data
9. Linear regression
10. Dimensionality reduction with principal component analysis
11. Density estimation with Gaussian mixture models
12. Classification with support vector machines.

Subject Areas: Pattern recognition [UYQP], Machine learning [UYQM], Maths for engineers [TBJ], Probability & statistics [PBT]