{{Short description|A paradigm in machine learning}}
'''Unsupervised learning''' is a method in [[machine learning]] where, in contrast to [[supervised learning]], algorithms learn patterns exclusively from unlabeled data.<ref name="WeiWu">{{Cite web |last=Wu |first=Wei |title=Unsupervised Learning |url=https://na.uni-tuebingen.de/ex/ml_seminar_ss2022/Unsupervised_Learning%20Final.pdf |access-date=26 April 2024 |archive-date=14 April 2024 |archive-url=https://web.archive.org/web/20240414213810/https://na.uni-tuebingen.de/ex/ml_seminar_ss2022/Unsupervised_Learning%20Final.pdf |url-status=live }}</ref> Within such an approach, a machine learning model tries to find any similarities, differences, patterns, and structure in data by itself. No prior human intervention is needed.<ref name="WeiWu" />
Other methods in the supervision spectrum are [[Reinforcement Learning]] where the machine is given only a numerical performance score as guidance,<ref>{{Cite web |last=Ghahramani |first=Zoubin |title=Unsupervised learning |url=https://mlg.eng.cam.ac.uk/pub/pdf/Gha03a.pdf |access-date=26 April 2024 |archive-date=12 November 2023 |archive-url=https://web.archive.org/web/20231112093614/https://mlg.eng.cam.ac.uk/pub/pdf/Gha03a.pdf |url-status=live }}</ref> and [[Weak_supervision | Weak or Semi supervision]] where a small portion of the data is tagged, and [[Self-supervised_learning | Self Supervision]].