A machine learning training method that trains a neural network by feeding it predefined sets of inputs and outputs. Supervised learning causes the network to learn by example. The network is fed pre-labeled input-output pairs so that it adjusts itself to recognize which input patterns produce which outputs.
Contrast with "unsupervised learning," whereby there are no input-output labels. The network differentiates the unknown input as a series of patterns. See machine learning
and deep learning