Outline of machine learning

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The following outline is provided as an overview of and topical guide to machine learning:

Machine learning – subfield of computer science[1] (more particularly soft computing) that evolved from the study of pattern recognition and computational learning theory in artificial intelligence.[1] In 1959, Arthur Samuel defined machine learning as a "Field of study that gives computers the ability to learn without being explicitly programmed".[2] Machine learning explores the study and construction of algorithms that can learn from and make predictions on data.[3] Such algorithms operate by building a model from an example training set of input observations in order to make data-driven predictions or decisions expressed as outputs, rather than following strictly static program instructions.

What type of thing is machine learning?[edit]

Branches of machine learning[edit]

Subfields[edit]

Cross-disciplinary fields[edit]

Machine learning hardware[edit]

Machine learning tools[edit]

Proprietary frameworks[edit]

Open source frameworks[edit]

Machine learning libraries[edit]

Machine learning methods[edit]

Supervised learning[edit]

Artificial neural network[edit]

Bayesian[edit]

Decision tree[edit]

Linear classifier[edit]

Unsupervised learning[edit]

Artificial neural network[edit]

Association rule learning[edit]

Hierarchical clustering[edit]

Cluster analysis[edit]

Anomaly detection[edit]

Semi-supervised learning[edit]

Reinforcement learning[edit]

Deep learning[edit]

Others[edit]

Applications of machine learning[edit]

Machine learning problems and tasks[edit]

Machine learning research[edit]

History of machine learning[edit]

Machine learning projects[edit]

Machine learning organizations[edit]

Machine learning venues[edit]

Machine learning conferences and workshops[edit]

Machine learning journals[edit]

Persons influential in machine learning[edit]

See also[edit]

Further reading[edit]

References[edit]

  1. ^ a b http://www.britannica.com/EBchecked/topic/1116194/machine-learning  This tertiary source reuses information from other sources but does not name them.
  2. ^ Phil Simon (March 18, 2013). Too Big to Ignore: The Business Case for Big Data. Wiley. p. 89. ISBN 978-1-118-63817-0. 
  3. ^ Ron Kohavi; Foster Provost (1998). "Glossary of terms". Machine Learning. 30: 271–274. 
  4. ^ http://www.learningtheory.org/

External links[edit]