The ancient problem of finding a function to map a set of observables (x) to a set of labels or values (y) has historically been solved solely and somewhat mysteriously by human brains through haphazard and still poorly understood thought processes. This problem is now routinely solved algorithmically by the machinery associated with supervised learning. The model architectures vary from very simple combinations of linear models, to contemporary neural networks, the targeted mapping (x→y) varies by application, and the training details depend on the complexity v vi of the task, but the essential ingredients are conserved. For problem after problem, provided sufficient data, ML has proven capable of learning reasonably accurate, and in many cases general, mapping functions of inputs to outputs.