'''Unsupervised learning''', refersis paradigm in [[machine learning]] where, in contrast to algorithms[[supervised thatlearning]] and [[semi-supervised learning]], algorithms learn patterns exclusively from unlabeled data.
In contrast to [[supervised learning]] where models learn to map the input to the target output (e.g. images labeled as a "cat" or "fish"), unsupervised methods learn concise representations of the input data, which can be used for data exploration or to analyze or generate new data. The other levels in the supervision spectrum are [[reinforcement learning]] where the machine is given only a "performance score" as guidance, and [[semi-supervised learning]] where only a portion of training data is labeled.
