ML courses often start with linear regression, and if you build up complicated polynomials to find a nonintuitive model of your problem, I would definitely consider that machine learning.
It looks like that's not the standard view, though.
Neural networks are just functional approximators, so why isn't a linear regressor of k-th order (e.g. Taylor expansion up to k-th order) also considered "ML"? What's the distinction here?