Machine learning, a branch of artificial intelligence, is a scientific discipline concerned with the development of algorithms that take as input empirical data (from sensors or databases), identify complex relationships, and employ these identified patterns to make predictions. The algorithm studies a portion of the observed data (called ‘training data’) to capture characteristics of interest. Optical character recognition, in which printed characters are recognized automatically based on previous examples, is a classic engineering example of machine learning.
In 1959, AI pioneer Arthur Samuel defined machine learning as a ‘Field of study that gives computers the ability to learn without being explicitly programmed.’ Computer scientist Tom M. Mitchell provided a widely quoted, more formal definition: ‘A computer program is said to learn from experience E with respect to some class of tasks T and performance measure P, if its performance at tasks in T, as measured by P, improves with experience E.’
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The Daily Omnivore 