Unsupervised learning

Unsupervised learning is a method in machine learning where, in contrast to supervised learning, algorithms learn patterns exclusively from unlabeled data. The hope is that through mimicry, which is an important mode of learning in people, the machine is forced to build a concise representation of its world and then generate imaginative content from it.

Other methods in the supervision spectrum are Reinforcement Learning where the machine is given only a numerical performance score as guidance, and Weak or Semi supervision where a small portion of the data is tagged, and Self Supervision.