Machine learning

Machine learning algorithms are organized into a taxonomy, based on the desired outcome of the algorithm. Common algorithm types include:

• supervised learning --- where the algorithm generates a function that maps inputs to desired outputs.
• unsupervised learning --- where the algorithm generates a model for a set of inputs.
• reinforcement learning --- where the algorithm learns a policy of how to act given an observation of the world.
• learning to learn --- where the algorithm learns its own inductive bias based on previous experience.

The performance and computational analysis of machine learning algorithms is a branch of statistics known as learning theory.

See also Important publications in machine learning.

Bibliography

• Mitchell, T. (1997). Machine Learning, McGraw Hill. ISBN 0070428077

Referenced By