Machine learning paradigms are very similar to the basic principles of human learning. Real learning has to do with extracting compact representations from the raw data which condense the regularities of the task to be learnt into a compact model. The model works when it generalizes properly for cases not considered during training (but extracted from the same probability distribution).
Generalization capabilities distinguish real learning from a trivial memorization(*) of the input examples (similar to “learning by heart” by poor students).
Our latest tutorial movie explains the basic methodology of machine learning in simple and human terms.
(*) Emphasis on “trivial”: memorizing all examples is not always a bad move, it depends on how you are going to use them later — see also our previous tutorial.