Mathematics for Machine Learning

by Garrett Thomas

A concise reference document that distills the essential linear algebra, calculus, optimization, and probability concepts needed for machine learning into a focused overview—ideal if you already have basic math background and want a quick refresher or lookup guide for the mathematical foundations underlying ML algorithms.

• Chapter 1
• Notation
• Linear Algebra
• Chapter 4
• Probability
• References