Machine learning is a multi-domain interdisciplinary subject involving probability theory, statistics, approximation theory, convex analysis, algorithm complexity theory and other disciplines. It specializes in how computers simulate or realize human learning behaviors to acquire new knowledge or skills, and to reorganize existing knowledge structures to continuously improve their performance.
It is the core of artificial intelligence and the fundamental way to make computers intelligent. It is applied in all fields of artificial intelligence. It mainly uses induction and synthesis instead of deduction.
Machine learning was proposed in 1990, 35 years after artificial intelligence (AL). Machine learning lets us solve some complex problems through algorithms. As artificial intelligence pioneer Arthur Samuel wrote in 1959, machine learning is a field of study that gives computers the ability to learn rather than explicitly program them.
The goal of most machine learning is to develop a predictive engine for a specific scenario. An algorithm will receive information for a domain. Case in point: movies a person has watched in the past, weighing the inputs to make a useful prediction (probability of a different movie to watch in the future).
The ability of computers to learn, measure the available data of variables through optimization tasks, and make algorithms to make accurate predictions about the future. Machines learn by training. The algorithm initially receives examples whose outputs are known, at which point it pays attention to the difference between its predictions and the correct output, and tunes the weights of the inputs to improve the accuracy of its predictions until they are optimized.
Therefore, the defining characteristic of a machine learning algorithm is that the quality of their predictions continually improves upon its experience.
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