Classification of objects is a fundamental and innate ability of our brain, which allows us to use a limited set of words to describe an almost infinite space of different objects, for instance, learning to classify food into nutritious or poisonous has been a key to the survival of organisms. The algorithmic and neuronal implementations of human classification are, however, not well understood. Why is it that a single example from a new class is sufficient to spawn a new category? Why and when do we generate new categories and how do we update them dynamically? How do our minds get so much from so little? We build rich models through which we make strong generalizations, and construct powerful abstractions, while the input data are noisy and often ambiguous. The impressive ease with which humans deal with these problems has been a major focus of the research community with many potential applications. In this talk, I will introduce recent approaches to reverse-engineering human category learning and discuss why gaining an understanding of the mechanisms that humans use to categorise data is essential for learning how the brain functions and how this knowledge can be used to create even more intelligent machines.