IMNSHO, as an undergrad, regardless of the field you end up choosing, you'd benefit from a solid introduction to axiomatic probability, a decent intro inferenti

IMNSHO, as an undergrad, regardless of the field you end up choosing, you'd benefit from a solid introduction to axiomatic probability, a decent intro inferential stats class that actually uses your foundational understanding of probability, a decent linear algebra class (so you know what an eigenvalue is and how PCA works, at the very least), and a decent class in mathematical analysis. That will give you the foundational knowledge to study any optimal decision making topic whether it is labeled as ML or AI as such. Most undergrad ML syllabi I have seen read like bad intro stats courses anyway. However, it never hurts to know enough terminology so that you avoid being overly impressed by snake oil salesmen later (and ML has more than its fair share of those).