I'm an experimental physicist by training and have used standard statistical methods to analyze data, and the design of experiments (DOE) framework to develop models of systems by varying inputs and measurement outputs.
Recently, I've been looking into the use of machine learning and I'm trying to figure if there's any utility/benefit over DOE.
I'm hoping someone on this forum can either validate the way I'm thinking about supervised machine learning, or point out what I'm missing.
I've basically come to the conclusion that supervised machine learning is a method to compute a transfer function of a system given the training data is a set of data that connects the set of inputs with what the output truth should be.
Notwithstanding the machinery that figures out the transfer function based on the training set, what is the difference between DOE and supervised machine learning in terms of the accuracy or other performance measure of the transfer function?