The name is a misnomer. And a harmful one, because it interferes with understanding the process that is really occuring.
What is really occurring is a search of a constrained program space. Let's say you want to be able to identify images of hot dogs. You begin with a plausible program for doing so, that is able to also search the space of nearby programs that might get better results on the problem. You then (in "supervised learning") provide scores that indicate how well one of these possible programs has done on solving the problem. After doing this for some time you settle upon a program that solves the problem "well enough."
This is a great technique that can produce truly impressive results on a wide class of problems, such as identifying images of hot dogs. But notice that, except for the phrase in scare quotes, there is no "learning" in the description. Calling this "learning" is importing ideological baggage that just obscures what is really going on.