WASP Mathematics for machine Learning 2022

Welcome to the WASP course Mathematics for Machine learning.

Practical information is found here.

Fortunately or not, mathematics in machine learning is unavoidable. The good thing is that it doesn't require much of it. The goal of this course is to give you a refresher (and sometimes even a new information) on most of the basic mathematical tools you might encounter when working with ML. We are going to cover four pillars: linear algebra, calculus, optimization, and probability. January lectures 24, 25 will consider the first two of them.

We follow (very roughly) the following book: 

M. P. Deisenroth, A. A. Faisal, and C. S. Ong.  "Mathematics for Machine Learning" (freely available at https://mml-book.github.io/)

To join the meetings, use zoom link https://liu-se.zoom.us/j/62410109270.