Definition of learning systems Machine Learning

 A computer program is said to learn from experience E with respect to some class of task T and performance measure P, if its performance at tasks in T, as measured by P, improves with experience E.

For example, a computer program that learns to play checkers might improve its performance as measured by its ability to win at the class of task involving playing checkers games, through experience obtained by playing against itself. In general, to have a well defined learning problem, we must identify these three features: the class of tasks, the measure of performance to be improved, and the source of experience.

Examples:

1. Checkers learning problem

• Task T: playing checkers
• Performance measure P: percent of games won against opponents 
• Training experience E: playing practice games against itself

       We can specify many learning problems in this fashion, such as learning to recognize handwritten words, or learning to drive a robotic automobile autonomously.

2. A handwritten recognition learning problem:

• Task T: recognizing and classifying handwritten words within images
• Performance measure P: percent of words correctly classified
• Training experience E: a database of handwritten words with given classifications

3. A robot driving learning problem:

• Task T: driving on public four lane highways using vision sensors
• Performance measure P: average distance travelled before an error
• Training experience E: a sequence of images and steering commands recorded while observing a human driver