Definitely linear algebra, but also some calculus and some probability theory &#x2F; statistics.  By way of illustration, if you were to go through the Andrew Ng course on Machine Learning, you&#x27;ll encounter mathematical explanations involving partial derivatives from differential calculus<p>As it is, he gives you the derivations you need, so you can complete the class without needing to find partial derivatives.  But the point is, a certain level of understanding of calculus will help with understanding exactly what is happening.<p>As for probability &#x2F; stats, you&#x27;ll find more than a few uses of probability notation and basic ideas from statistics scattered throughout machine learning.