Machine learning

Study of algorithms that improve automatically through experience / From Wikipedia, the free encyclopedia

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Machine learning (ML) is a field of study in artificial intelligence concerned with the development and study of statistical algorithms that can learn from data and generalize to unseen data, and thus perform tasks without explicit instructions.[1] Recently, generative artificial neural networks have been able to surpass many previous approaches in performance.[2][3]

Machine learning approaches have been applied to many fields including large language models, computer vision, speech recognition, email filtering, agriculture, and medicine, where it is too costly to develop algorithms to perform the needed tasks.[4][5] ML is known in its application across business problems under the name predictive analytics. Although not all machine learning is statistically based, computational statistics is an important source of the field's methods.

The mathematical foundations of ML are provided by mathematical optimization (mathematical programming) methods. Data mining is a related (parallel) field of study, focusing on exploratory data analysis through unsupervised learning.[7][8] From a theoretical point of view Probably approximately correct learning provides a framework for describing machine learning.

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