ah ok. I am also starting with ML, so take this with a grain of salt.
While the courses i listed are required for most CS majors, i think they are more important for ML than many other fields of CS (of course there are exceptions). I think a deeper understanding of LA, Calculus and Numeric compared to normal CS-majors is beneficial (but if you majored in math you probably had to take these in more depth than most CS-majors). Also i would double down on Probability (so a course on Probability-Theory, Markov-Chains etc.). Optimisation Theory is also important, but often already somewhat covered in the other courses. I don't think one can point to a specific course that will be really important for NN in the future, nobody really knows whats coming. Just learn as much math as possible, so that you are prepared to learn and understand all the future theory and techniques that are not yet discovered. Maybe we will all be solving stochastic differential equations in the future, because they accurately predict the performance of various NNs?
Edit: I forgot tensor-mathematics, i haven't taken a course yet, but it seems pretty straightforward.
While the courses i listed are required for most CS majors, i think they are more important for ML than many other fields of CS (of course there are exceptions). I think a deeper understanding of LA, Calculus and Numeric compared to normal CS-majors is beneficial (but if you majored in math you probably had to take these in more depth than most CS-majors). Also i would double down on Probability (so a course on Probability-Theory, Markov-Chains etc.). Optimisation Theory is also important, but often already somewhat covered in the other courses. I don't think one can point to a specific course that will be really important for NN in the future, nobody really knows whats coming. Just learn as much math as possible, so that you are prepared to learn and understand all the future theory and techniques that are not yet discovered. Maybe we will all be solving stochastic differential equations in the future, because they accurately predict the performance of various NNs?
Edit: I forgot tensor-mathematics, i haven't taken a course yet, but it seems pretty straightforward.