====== Machine Learning (ML) ======
[[dido:public:ra:xapend:xapend.a_glossary:start| Return to Glossary ]]

**[[https://en.wikipedia.org/wiki/Machine_learning | Machine Learning (ML)]]** is the study of computer algorithms that improve automatically through experience and by the use of data. ((
Mitchell, Tom (1997). 
__Machine Learning. New York: McGraw Hill__. 
ISBN 0-07-042807-7. OCLC 36417892.
[[http://www.cs.cmu.edu/~tom/mlbook.html]]
)) It is seen as a part of [[dido:public:ra:xapend:xapend.a_glossary:a:ai]]. Machine learning algorithms build a model based on sample data, known as "training data", in order to make predictions or decisions without being explicitly programmed to do so.((
The definition "without being explicitly programmed" is often attributed to Arthur Samuel, who coined the term "machine learning" in 1959, but the phrase is not found verbatim in this publication, and maybe a paraphrase that appeared later. Confer "Paraphrasing Arthur Samuel (1959), the question is: How can computers learn to solve problems without being explicitly programmed?" in Koza, John R.; Bennett, Forrest H.; Andre, David; Keane, Martin A. (1996). 
__Automated Design of Both the Topology and Sizing of Analog Electrical Circuits Using Genetic Programming__. 
Artificial Intelligence in Design '96. Springer, Dordrecht. pp. 151–170. 
doi:10.1007/978-94-009-0279-4_9.
[[https://doi.org/10.1007%2F978-94-009-0279-4_9]]
)) Machine learning algorithms are used in a wide variety of applications, such as in medicine, email filtering, and computer vision, where it is difficult or unfeasible to develop conventional algorithms to perform the needed tasks.((
Hu, J.; Niu, H.; Carrasco, J.; Lennox, B.; Arvin, F., 
__Voronoi-Based Multi-Robot Autonomous Exploration in Unknown Environments via Deep Reinforcement Learning__ 
IEEE Transactions on Vehicular Technology, 2020.
[[https://ieeexplore.ieee.org/document/9244647]]
))

Source: [[ https://en.wikipedia.org/wiki/Glossary_of_artificial_intelligence ]]


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