Things like constraint solvers, automata, Bayesian reasoning & other probability/stats topics, etc. may also come in handy, but the core is mostly applied linear algebra.
Also, math will help you understand machine learning algorithms, but if you want to be a practitioner, most of the hard work is in feature selection, data cleaning, backtesting, etc. These don't need a deep understanding of math so much as a deep understanding of your data - 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.