[ragulpr]

* Solid linear algebra

* Probability theory

* Statistics (bayesian if possible)

* Optimization (this is less important but extremely useful)

If you know the mechanics of multivariate calculus you'll be fine learning the above. The course that personally have had most payoff was functional analysis. Purely theoretical course that will give you no practical skills and at first glance seems unrelated to ML but it (subtly) gave me a much deeper understanding of what ML is all about.