As someone who doesn't particularly like math but thinks robotics is cool, I had the horror of having to encounter this type of thing: https://www.rosroboticslearning.com/jacobian

Robotics also requires a thorough understanding rotations and transformations (and its time derivatives), thus requiring a bit of Lie algebra. The calculations itself aren’t that complicated, but the concept is still quite confusing for beginners and quite intimidating if you go deep down into theory.

In addition, almost any control system with feedback requires some variant of PID control. “I” as in integral and “D” as in derivative makes it hard to get away from calculus concepts

I took a grad robotics course while in grad school for another degree. Doing all the translation matrix multiplies by hand was so much fun before something like Matlab existed. (As was going to the computer lab at 3am to do the simulations because that was the only time there was time available.)

For ML you really need both a class on probability theory, linear algebra, and/or a dedicated ML class. Lots of ML math ends up being the same principals over and over again. They may be borrowed from bigger subject matter, but often times you don't need to be an expert in those to get far. That said, if you're an ML researcher, then you definitely benefit from a very solid math/physics background. But if you're an ML researcher, chances are you did not only undergrad but a PhD as well, and you're not just considering an online certificate.

You need to know what grad is, that's Calc III. Is SVD even covered in Linear Algebra I? Convergence rate is also something you should understand.

This stuff is all pretty deep into undergrad curricula.