Editing Machine learning (section)

=== Artificial intelligence ===
[[File:AI hierarchy.svg|thumb|Machine learning as subfield of AI<ref name="journalimcms.org">{{cite journal |vauthors=Sindhu V, Nivedha S, Prakash M |date=February 2020|title=An Empirical Science Research on Bioinformatics in Machine Learning |journal=Journal of Mechanics of Continua and Mathematical Sciences |issue=7 |doi=10.26782/jmcms.spl.7/2020.02.00006 |doi-access=free}}</ref>]]
As a scientific endeavour, machine learning grew out of the quest for [[artificial intelligence]] (AI). In the early days of AI as an [[Discipline (academia)|academic discipline]], some researchers were interested in having machines learn from data. They attempted to approach the problem with various symbolic methods, as well as what were then termed "[[Artificial neural network|neural network]]s"; these were mostly [[perceptron]]s and [[ADALINE|other models]] that were later found to be reinventions of the [[generalised linear model]]s of statistics.<ref>{{cite book |last1=Sarle |first1=Warren S.|chapter=Neural Networks and statistical models |pages=1538–50 |year=1994 |title=SUGI 19: proceedings of the Nineteenth Annual SAS Users Group International Conference |publisher=SAS Institute |isbn=9781555446116 |oclc=35546178}}</ref> [[Probabilistic reasoning]] was also employed, especially in [[automated medical diagnosis]].<ref name="aima">{{cite AIMA|edition=2}}</ref>{{rp|488}}

However, an increasing emphasis on the [[symbolic AI|logical, knowledge-based approach]] caused a rift between AI and machine learning. Probabilistic systems were plagued by theoretical and practical problems of data acquisition and representation.<ref name="aima" />{{rp|488}} By 1980, [[expert system]]s had come to dominate AI, and statistics was out of favour.<ref name="changing">{{Cite journal | last1 = Langley | first1 = Pat| title = The changing science of machine learning | doi = 10.1007/s10994-011-5242-y | journal = [[Machine Learning (journal)|Machine Learning]]| volume = 82 | issue = 3 | pages = 275–9 | year = 2011 | doi-access = free }}</ref> Work on symbolic/knowledge-based learning did continue within AI, leading to [[inductive logic programming]](ILP), but the more statistical line of research was now outside the field of AI proper, in [[pattern recognition]] and [[information retrieval]].<ref name="aima" />{{rp|708–710; 755}} Neural networks research had been abandoned by AI and [[computer science]] around the same time. This line, too, was continued outside the AI/CS field, as "[[connectionism]]", by researchers from other disciplines including [[John Hopfield]], [[David Rumelhart]], and [[Geoffrey Hinton]]. Their main success came in the mid-1980s with the reinvention of [[backpropagation]].<ref name="aima" />{{rp|25}}

Machine learning (ML), reorganised and recognised as its own field, started to flourish in the 1990s. The field changed its goal from achieving artificial intelligence to tackling solvable problems of a practical nature. It shifted focus away from the [[symbolic artificial intelligence|symbolic approaches]] it had inherited from AI, and toward methods and models borrowed from statistics, [[fuzzy logic]], and [[probability theory]].<ref name="changing" />