Machine learning is an area of artificial intelligence involving developing techniques to allow computers to "learn". More specifically, machine learning is a method for creating computer programs by the analysis of data sets, rather than the intuition of engineers.
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
