Supervised learning in machine learning is nothing remotely like a human teaching anyone anything. It's a very clear mathematical formulation of what the objective is and how the algorithm can improve itself against that objective.
The closest analogy for humans would be to define a metric and ask a human to figure out how to maximize that metric. That's something we're often pretty good at doing, often in ways that the person defining the metric didn't actually want us to use.
The closest analogy for humans would be to define a metric and ask a human to figure out how to maximize that metric. That's something we're often pretty good at doing, often in ways that the person defining the metric didn't actually want us to use.