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'''Machine learning''' ('''ML''') is a [[field of study]] in [[artificial intelligence]] concerned with the development and study of [[Computational statistics|statistical algorithms]] that can learn from [[data]] and [[generalise]] to unseen data, and thus perform [[Task (computing)|tasks]] without explicit [[Machine code|instructions]].{{Refn|The definition "without being explicitly programmed" is often attributed to [[Arthur Samuel (computer scientist)|Arthur Samuel]], who coined the term "machine learning" in 1959, but the phrase is not found verbatim in this publication, and may be 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 {{Cite conference |chapter=Automated Design of Both the Topology and Sizing of Analog Electrical Circuits Using Genetic Programming |conference=Artificial Intelligence in Design '96 |last1=Koza |first1=John R. |last2=Bennett |first2=Forrest H. |last3=Andre |first3=David |last4=Keane |first4=Martin A. |title=Artificial Intelligence in Design '96 |date=1996 |publisher=Springer Netherlands |___location=Dordrecht, Netherlands |pages=151–170 |language=en |doi=10.1007/978-94-009-0279-4_9 |isbn=978-94-010-6610-5 }}}} Within a subdiscipline in machine learning, advances in the field of [[deep learning]] have allowed [[Neural network (machine learning)|neural networks]], a class of statistical algorithms, to surpass many previous machine learning approaches in performance.<ref name="ibm">{{Cite web |title=What is Machine Learning? |url=https://www.ibm.com/topics/machine-learning |access-date=27 June 2023 |website=IBM |date=22 September 2021 |language=en-us |archive-date=27 December 2023 |archive-url=https://web.archive.org/web/20231227153910/https://www.ibm.com/topics/machine-learning |url-status=live }}</ref>
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Machine learning (ML) allows computers to learn and make decisions without being explicitly programmed. It involves feeding data into algorithms to identify patterns and make predictions on new data. It is utilised in various applications including image recognition, speech processing, language translation, and recommender systems.
ML finds application in many fields, including [[natural language processing]], [[computer vision]], [[speech recognition]], [[email filtering]], [[agriculture]], and [[medicine]].<ref name="tvt">{{Cite journal |last1=Hu |first1=Junyan |last2=Niu |first2=Hanlin |last3=Carrasco |first3=Joaquin |last4=Lennox |first4=Barry |last5=Arvin |first5=Farshad |date=2020 |title=Voronoi-Based Multi-Robot Autonomous Exploration in Unknown Environments via Deep Reinforcement Learning |journal=IEEE Transactions on Vehicular Technology |volume=69 |issue=12 |pages=14413–14423 |doi=10.1109/tvt.2020.3034800 |s2cid=228989788 |issn=0018-9545 |doi-access=free |url=https://research.manchester.ac.uk/files/191737243/09244647.pdf }}</ref><ref name="YoosefzadehNajafabadi-2021">{{cite journal |last1=Yoosefzadeh-Najafabadi|first1=Mohsen |last2=Hugh |first2=Earl |last3=Tulpan |first3=Dan |last4=Sulik |first4=John |last5=Eskandari |first5=Milad |title=Application of Machine Learning Algorithms in Plant Breeding: Predicting Yield From Hyperspectral Reflectance in Soybean? |journal=Front. Plant Sci. |volume=11 |year=2021 |pages=624273|doi=10.3389/fpls.2020.624273 |pmid=33510761 |pmc=7835636 |doi-access=free |bibcode=2021FrPS...1124273Y }}</ref> The application of ML to business problems is known as [[predictive analytics]].
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