Supervised learning: for each example we have a label, and we’ll find a way to predict that label associated with the input.
Unsupervised: we have a set of feature vectors without labels, and we’ll try to group them into “natural clusters” (or labels for those groups). In some cases we’ll know how many labels there should be, in some other cases we’ll find which is the best number of them.