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.

See also:

• unsupervised ML

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

Unsupervised learning refers to machine learning methods that learn a function without relying on concretely specified targets (e.g. ''labels''). There are different opinions as to the degree of concreteness of external targets that can no longer be referred to as unsupervised learning.

See also:

• supervised ML

ETSI GR ENI 004 learning a function that maps an input to an output without the benefit of the data being classified or labelled

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

ISO/IEC TR 29119-11:2020 task of learning a function that maps unlabelled input data to a latent representation

ISTQB - CTAI Syllabus Training an ML model from input data using an unlabeled dataset