Machine learning is a data analysis technique to discover (complex) patterns in datasets related to some property of interest. For example, one may wish to automatically recognize the content of a picture and use that to label or categorize it. These techniques have found their application in many (research) areas, ranging from physics, biology, medicine, and, of course, humanities. In this talk I will give an introduction to what machine learning is, when and how it can (and should) be used, and what it can do – for us. I will take you through some appealing (and appalling!) examples from the literature along with examples from my own research (neuroimaging in psychiatry), as well as current use and potential applications within humanities research. It will turn out that, without knowing it, many researchers have already used some kind of machine learning. While machine learning is a powerful tool to extract useful information from data and obtain valuable insights, it should be used with care – like any statistics. Common pitfalls are discussed and I will argue that human intelligence is still necessary to obtain valid and reliable results.