Arthur Samuel described it as : "Machine learning is the science of getting computers 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.
Application of machine learning:
- Database mining
- Applications can't program by hand (NLP,CV)
- Self-customizing programs
- Understanding human learning
In general, any machine learning problem can be assigned to one of two broad classifications:
- Supervised learning
- Unsupervised learning