Linear algebra is to machine learning as flour to bakery: every machine learning model is based in linear algebra, as every cake is based in flour. It is not the only ingredient, of course. Machine learning models need vector calculus, probability, and optimization, as cakes need sugar, eggs, and butter. Applied machine learning, like bakery, is essentially about combining these mathematical ingredients in clever ways to create useful (tasty?) models.

This document contains introductory level linear algebra notes for applied machine learning. It is meant as a reference rather than a comprehensive review. … The notes are based on a series of (mostly) freely available textbooks, video lectures, and classes I’ve read, watched and taken in the past. If you want to obtain a deeper understanding or to find exercises for each topic, you may want to consult those sources directly. Read More