In order to relate machine learning problems we argue that we need to be able to articulate what is meant by a single machine learning problem. By attempting to name the various aspects of a learning problem we hope to clarify ways in which learning problems might be related to each other. We tentatively put forward a proposal for an anatomy of learning problems that will serve as scaffolding for posing questions about relations. After surveying the way learning problems are discussed in a range of repositories and services. We then argue that the terms used to describe problems to better understand a range of viewpoints within machine learning ranging from the theoretical to the practical.