> recommendations A lot of this is standard statistical methods, much of which are much older than twenty years. Really, that's the part that threw me, twenty

> recommendations A lot of this is standard statistical methods, much of which are much older than twenty years. Really, that's the part that threw me, twenty years ago everyone was going crazy for SVM's, which is still machine learning, but the features were definitely hand crafted. I think deep learning has been super successful with unstructured data, but for tabular data it's pretty much a wash between NN's and boosted trees or generalised additive models. You do need some flexibility in your function approximation, but not as much as people commonly believe.