In supervised learning an algorithm is given an input and a desired output, and the aim is to learn the mapping from input to output. Think of it as a school test, where there are questions and answers, and you are graded by how close your answers are to the actual ones.
Now imagine there are no answers, what can you learn if you only have the questions? In unsupervised learning the aim is to uncover the underlying structure or distribution of the data in order to learn more about it. For example, finding groups of users that behave in the same way, or identifying events that are anomalous. Unsupervised learning can even generate completely new data. If given enough images of faces, we can train a computer to create realistic images of people who don’t even exist.