Machine learning
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 overlaps heavily with statistics, since both fields study the analysis of data.
Machine learning has a wide spectrum of applications including medical diagnosis, detecting credit card fraud, stock market analysis, classifying DNA sequences, speech and handwriting recognition, game playing and robot locomotion.
The standard formulation of the learning task is the 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
Bibliography
- Bishop, C. M. (1995). Neural Networks for Pattern Recognition, Oxford University Press. ISBN 0198538642
