Machine learning

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Machine learning is an area of artificial intelligence concerned with the development of techniques which allow computers to "learn". More specifically, machine learning is a method for creating computer programs by the analysis of data sets. Machine learning overlaps heavily with statistics, since both fields study the analysis of data, but unlike statistics, machine learning is concerned with the algorithmic complexity of computational implementations. Many inference problems turn out to be NP-hard or harder, so part of machine learning research is the development of tractable approximate inference algorithms.

Machine learning has a wide spectrum of applications including search engines, medical diagnosis, detecting credit card fraud, stock market analysis, classifying DNA sequences, speech and handwriting recognition, object recognition in computer vision, game playing and robot locomotion.

1 Human interaction
2 Algorithm types
3 Machine learning topics
4 See also
5 Bibliography
6 External links

Human interaction

Some machine learning systems attempt to eliminate the need for human intuition in the analysis of the data, while others adopt a collaborative approach between human and machine. Human intuition cannot be entirely eliminated since the designer of the system must specify how the data are to be represented and what mechanisms will be used to search for a characterization of the data. Machine learning can be viewed as an attempt to automate parts of the scientific method. Some machine learning researchers create methods within the framework of Bayesian statistics.

Algorithm types

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 $[X_1, X_2, \ldots X_N]\,$ 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.
semi-supervised learning --- which combines both labeled and unlabeled examples to generate an appropriate function or classifier.
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.
transduction --- similar to supervised learning, but does not explicitly construct a function: instead, tries to predict new outputs based on training inputs, training outputs, and new inputs.
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 computational learning theory.