(18th-May-2020)
• Virtually any task for which an agent can get data or experiences can be learned. The most commonly studied learning task is supervised learning: given some input features, some target features, and a set of training examples where the input features and the target features are specified, predict the target features of a new example for which the input features are given. This is called classification when the target variables are discrete and regression when the target features are continuous.
• Other learning tasks include learning classifications when the examples are not already classified (unsupervised learning), learning what to do based on rewards and punishments (reinforcement learning), learning to reason faster (analytic learning), and learning richer representations such as logic programs (inductive logic programming) or Bayesian networks.
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• Feedback
• Learning tasks can be characterized by the feedback given to the learner. In supervised learning, what has to be learned is specified for each example. Supervised classification occurs when a trainer provides the classification for each example. Supervised learning of actions occurs when the agent is given immediate feedback about the value of each action. Unsupervised learning occurs when no classifications are given and the learner must discover categories and regularities in the data. Feedback often falls between these extremes, such as in reinforcement learning, where the feedback in terms of rewards and punishments occurs after a sequence of actions. This leads to the credit-assignment problem of determining which actions were responsible for the rewards or punishments.
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