Mathematics for Machine Learning

Companion webpage to the book "Mathematics for Machine Learning". Copyright 2018 by Marc Peter Deisenroth, A Aldo Faisal, and Cheng Soon Ong. To be published by Cambridge University Press.

View the Project on GitHub

Please link to this site using

Twitter:@mpd37, @AnalogAldo, @ChengSoonOng.

We are in the process of writing a book on Mathematics for Machine Learning that motivates people to learn mathematical concepts. The book is not intended to cover advanced machine learning techniques because there are already plenty of books doing this. Instead, we aim to provide the necessary mathematical skills to read those other books.

We split the book into two parts:

We aim to keep this book fairly short (tried for 300 pages, now close to 400 pages), so we don’t cover everything.

We will keep PDFs of this book freely available after publication.

Report errata and feedback.

Draft chapters for download


Part I: Mathematical Foundations

  1. Introduction and Motivation
  2. Linear Algebra
  3. Analytic Geometry
  4. Matrix Decompositions
  5. Vector Calculus
  6. Probability and Distribution
  7. Continuous Optimization

Part II: Central Machine Learning Problems

  1. When Models Meet Data
  2. Linear Regression
  3. Dimensionality Reduction with Principal Component Analysis
  4. Density Estimation with Gaussian Mixture Models
  5. Classification with Support Vector Machines



Report errata and feedback.