Say we have a known model $M$ with unknown parameters and more specifically, $M$ is a parametric model.
Parameter estimation on $M$ is applying an appropriate method for estimating the parameters.
My question is: If we know the form/shape of the model $M$ up to unknown parameters, and applying a machine learning algorithm such as gradient descent, does this fall under machine/statistical learning?
Or is it still only parameter estimation using a borrowed method since the model form is known?
It seems the former question is yes since we are "learning" the parameters rather than just doing some algebraic/calculus manipulation to get suitable estimators directly.
My confusion comes from that what reading I have done on machine learning/statistical learning is that the goal is to estimate the model itself or rather the function $f$ which gives the model. Of course, parameter estimation is something utilized in machine/statistical learning but seems the situation I described is a instance of machine/statistical learning, perhaps this instance would be more specifically referred to as parametric machine/statistical learning