Editing Machine learning (section)

=== Unsupervised learning ===
{{Main|Unsupervised learning}}{{See also|Cluster analysis}}
Unsupervised learning algorithms find structures in data that has not been labelled, classified or categorised. Instead of responding to feedback, unsupervised learning algorithms identify commonalities in the data and react based on the presence or absence of such commonalities in each new piece of data. Central applications of unsupervised machine learning include clustering, [[dimensionality reduction]],<ref name="Friedman-1998" /> and [[density estimation]].<ref name="JordanBishop2004">{{cite book |first1=Michael I. |last1=Jordan |first2=Christopher M. |last2=Bishop |chapter=Neural Networks |editor=Allen B. Tucker |title=Computer Science Handbook, Second Edition (Section VII: Intelligent Systems) |location=Boca Raton, Florida |publisher=Chapman & Hall/CRC Press LLC |year=2004 |isbn=978-1-58488-360-9 }}</ref>

Cluster analysis is the assignment of a set of observations into subsets (called ''clusters'') so that observations within the same cluster are similar according to one or more predesignated criteria, while observations drawn from different clusters are dissimilar. Different clustering techniques make different assumptions on the structure of the data, often defined by some ''similarity metric'' and evaluated, for example, by ''internal compactness'', or the similarity between members of the same cluster, and ''separation'', the difference between clusters. Other methods are based on ''estimated density'' and ''graph connectivity''.

A special type of unsupervised learning called, [[self-supervised learning]] involves training a model by generating the supervisory signal from the data itself.<ref>{{Cite conference|last1=Misra |first1=Ishan |last2=Maaten |first2=Laurens van der |date=2020 |title=Self-Supervised Learning of Pretext-Invariant Representations |url=https://openaccess.thecvf.com/content_CVPR_2020/html/Misra_Self-Supervised_Learning_of_Pretext-Invariant_Representations_CVPR_2020_paper.html |publisher=[[Institute of Electrical and Electronics Engineers|IEEE]] |pages=6707–6717 |conference=2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) |doi=10.1109/CVPR42600.2020.00674 |location=Seattle, WA, USA |arxiv=1912.01991 }}</ref><ref>{{Cite journal |last1=Jaiswal |first1=Ashish |last2=Babu |first2=Ashwin Ramesh |last3=Zadeh |first3=Mohammad Zaki |last4=Banerjee |first4=Debapriya |last5=Makedon |first5=Fillia |date=March 2021 |title=A Survey on Contrastive Self-Supervised Learning |journal=Technologies |language=en |volume=9 |issue=1 |pages=2 |doi=10.3390/technologies9010002 |doi-access=free |issn=2227-7080|arxiv=2011.00362 }}</ref>