[fnbr]

A lot of probability theory requires it. For instance, ML is largely framed mathematically as a series of optimisation problem, which are then solved by finding the gradient and performing gradient descent; this requires elementary calculus to calculate the gradient.

Additionally, if you want to calculate a probability given a density function, or evaluate an expectation, you need to calculate several integrals. This arises quite often in the theoretical sections of ML papers/textbooks.

The use of calculus in ML is probably similar to the use of number theory in crypto- you can do applied work fine without it, but you understand the work a lot better by knowing the math, and are less likely to make dumb mistakes.