The three main settings in which a machine learning system is trained. In supervised learning the algorithm learns a mapping from inputs to outputs using labelled examples, in unsupervised learning it finds structure in data that carries no labels, and in reinforcement learning an agent learns which actions to take by receiving rewards or penalties from its environment as it acts. Together they cover most of the ways in which a system can be taught from data, from fully labelled examples to feedback received only after a series of choices.
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Machine Learning Paradigms
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Supervised learning - Wikipedia
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In machine learning, supervised learning (SL) is a type of machine learning paradigm where an algorithm learns to map input data to a specific output based on example input-output pairs.
Unsupervised learning - Wikipedia
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Unsupervised learning is a framework in machine learning where, in contrast to supervised learning, algorithms learn patterns exclusively from unlabeled data.
Reinforcement learning, Wikipedia
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In machine learning and optimal control, reinforcement learning (RL) (also known as approximate dynamic programming) is concerned with how an intelligent agent should take actions in a dynamic environment in order to maximize a reward signal.
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