desired outputs, predict outputs on future inputs.
that maximize expected future rewards. - Active inference: Given an observed sequence of input signals and a prior probability distribution about future observations, learn to select actions that minimize expected prediction errors (i.e., minimize actual minus predicted sensation).
Given data $D=\{x_1,\ldots,x_N\}$, model the (unconditional) probability distribution $p(x)$ (a.k.a. density estimation). The two primary applications are clustering and compression (a.k.a. dimensionality reduction).
Given the state of the world (obtained from sensory data), the agent must learn to produce actions that optimize some performance criterion about expected future.
computer speech recognition, speaker recognition
face recognition, iris identification
printed and handwritten text parsing
financial prediction, outlier detection (credit-card fraud)
user preference modeling (amazon); modeling of human perception
modeling of the web (google)
machine translation
medical expert systems for disease diagnosis (e.g., mammogram)
strategic games (chess, go, backgammon), self-driving cars
In summary, any 'knowledge-poor' but 'data-rich' problem
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