The "learning" part is about using a particular model to be able to give you a good performance on your sample (read: small

), with the consequence that

will also be reduced, based on certain properties and principles explained in the lectures.

Take the example of the convex set. Your points are scattered on a 2D plane, which means we have 2 features (e.g. the diameter and weight of coins, if you want a concrete example). Suppose you want to try to model only a single class of coins (e.g. dimes). You can then bound all the points in the graph that correspond to dimes with a convex set. Say you come up with a triangle; now your model can be described with 3 points -- one for each vertex of your triangle.