[rocqua]

Much of ML is optimization. This is linked to calculus by derivatives. There is the simple part that at a minimum or maximum the derivative is 0. However, more relevance comes from gradient descent. This depends very heavily on calculating derivatives, and its one of the most universal fast optimization methods.

Beyond that, for iterative methods, convergence is a matter of limits. This again is calculus. Formulating iteration as repeatedly applying a function, we converge to a fixed point of that function if and only if the derivative at that fixed point lies between -1 and 1. Again derivatives come in.

Finally, for error estimation, taylor-expansions are often useful. Again, the topic here is calculus. Notably, all I can think of regards limits and derivatives, not integrals. That might just be due to my hatred of integrals though.