# (Linear-algebraic) Mathematical preliminaries for machine learning

Carnegie Mellon runs a nice foundational course in machine learnining called 10-715: Advanced Introduction to Machine Learning. While following the Fall 2017 version of the course, I decided to supply a quick set of notes on the mathematical preliminaries for the course material. I ended up mostly writing about linear algebra, as I did not have the time to finish the section on probability theory.

Download the notes here: GitHub