The learning algorithm of a neural network can either be supervised or unsupervised.
A neural net is said to learn supervised if the desired output is already known. Example: pattern association
Neural nets that learn unsupervised have no such target outputs. It can't be determined what the result of the learning process will look like. During the learning process, the units (weight values) of such a neural net are "arranged" inside a certain range, depending on given input values. The goal is to group similar units close together in certain areas of the value range. Example: pattern classification
