  The areas of machine learning and knowledge discovery in databases have
considerably matured in recent years. In this article, we briefly review recent
developments as well as classical algorithms that stood the test of time. Our
goal is to provide a general introduction into different tasks such as learning
from tabular data, behavioral data, or textual data, with a particular focus on
actual and potential applications in behavioral sciences. The supplemental
appendix to the article also provides practical guidance for using the methods
by pointing the reader to proven software implementations. The focus is on R,
but we also cover some libraries in other programming languages as well as
systems with easy-to-use graphical interfaces.
