A subset of artificial intelligence that enables computers to learn and improve from experience without being explicitly programmed for every task.
Term coined by IBM researcher Arthur Samuel in 1959, who defined it as giving computers 'the ability to learn without being explicitly programmed.' Built on statistical learning theory developed in the early 20th century but became practical with increased computing power and data availability.
Machine learning algorithms essentially find patterns in data that humans would miss or take too long to discover, but they're only as good as the data they're trained on - leading to the popular saying 'garbage in, garbage out.' The most successful applications often involve problems with clear success metrics and abundant training data.
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