In a narrower sense, supervised learning refers to machine learning methods that are trained with concretely specified target outputs (so-called ''labels''). In a broader sense, it includes methods whose learning goal is determined by concrete specifications, even if not at the level of individual outputs. This broader sense includes procedures such as GANs and reinforcement learning.

• unsupervised ML

ISO/IEC DIS 22989 machine learning (3.2.9) that makes use of labelled data during training (3.2.21)