Linear algebra.<p>Things like constraint solvers, automata, Bayesian reasoning &amp; other probability&#x2F;stats topics, etc. may also come in handy, but the core is mostly applied linear algebra.<p>Also, math will help you understand machine learning <i>algorithms</i>, but if you want to be a practitioner, most of the hard work is in feature selection, data cleaning, backtesting, etc.  These don&#x27;t need a deep understanding of math so much as a deep understanding of <i>your data</i> - key skills there include graphing data; having an intuition for different statistical distributions; being able to build a webapp that lets you easily graph a candidate feature, drill into examples, and share the results with the rest of your team, and other very mundane tasks that are pretty basic software engineering with a stats focus.