> Most machine learning models are nothing but souped up regression that swaps out standard polynomials for matrix-valued polynomials with some x'es replaced by

> Most machine learning models are nothing but souped up regression that swaps out standard polynomials for matrix-valued polynomials with some x'es replaced by non-linear activation functions like tanh(x) Well, not quite. The non-linear activation feature is found already in "generalized linear models" (as a 'link function'). The typical "deep learning" model is a hierarchical/multi-level version of the same - and just like any other hierarchical/multi-level model, the point of that is to account in a parsimonious way for increasingly-complicated interactions among the regressors/features. But I think your broader point is right; there is a lot of hype in ML that derives purely from a lack of familiarity with basic statistical principles.