Two definitions of Machine Learning are offered. Arthur Samuel described it as:
the field of study that gives computers the ability to learn without being explicitly programmed.
This is an older, informal definition.
Tom Mitchell provides a more modern definition:
A computer program is said to learn from experience E with respect to some class of tasks T and performance measure P, if its performance at tasks in T, as measured by P, improves with experience E.
Example: playing checkers.
E = the experience of playing many games of checkers
T = the task of playing checkers.
P = the probability that the program will win the next game.
In general, any machine learning problem can be assigned to one of two broad classifications: