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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.

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. One standard formulation of the supervised learning task is the classification problem: the learner is required to learn (to approximate the behavior of) a function which maps a vector into one of several classes by looking at several input-output examples of the function.
• unsupervised learning --- which models a set of inputs: labeled examples are not available.
• reinforcement learning --- where the algorithm learns a policy of how to act given an observation of the world. Every action has some impact in the environment, and the environment provides feedback that guides the learning algorithm.
• learning to learn --- where the algorithm learns its own inductive biasInformally speaking, the inductive bias of a machine learning algorithm refers to additional assumptions, that the learner will use to predict correct outputs for situations that have not been encountered so far. In machine learning one aims at the constr based on previous experience.

The performance and computational analysis of machine learning algorithms is a branch of statistics known as computational learning theoryIn statistics, computational learning theory is a mathematical field related to the analysis of machine learning algorithms. Machine learning algorithms take a training set, form hypotheses or models, and make predictions about the future. Because the tra.

1 See also

• important publications in machine learning(computer science)
• Important publications in machine learning(statistics)Probability The Doctrine of Chances Author Abraham de Moivre Publication data 1738 (2nd ed. Online version ? Description The book introduced the concept of normal distributions as approximations to binomial distributions. In effect, de Moivre proved a wea

2 Bibliography

• Bishop, C. M. (1995). Neural Networks for Pattern Recognition, Oxford University Press. BooksEnthsiast.com

• MacKay, D. J. C. (2003). Information Theory, Inference, and Learning Algorithms, Cambridge University Press. BooksEnthsiast.com

• Mitchell, T. (1997). Machine Learning, McGraw Hill. BooksEnthsiast.com