In supervised learning the agent observes some example input–output pairs and learns a function that maps from input to output
Key points of Supervised learning
- In this learning, every input pattern that is used to train the network is associated with an output pattern
- This is called ”training set of data”. Thus, in this form of learning, the input-output relationship of the training scenarios are available
- Here, the output of a network is compared with the corresponding target value and the error is determined
- It is then feed back to the network for updating the same. This results in an improvement.
In unsupervised learning the agent learns patterns in the input even though no explicit feedback is supplied. The most common unsupervised learning task is clustering: detecting potentially useful clusters of input examples.
For example, a taxi agent might gradually develop a concept of “good traffic days” and “bad traffic days” without ever being given labeled examples of each by a teacher
Key points of unsupervised learning
- If the target output is not available, then the error in prediction can not be determined and in such a situation, the system learns of its own by discovering and adapting to structural features in the input patterns.
- This type of training is called learning without a teacher.