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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 of machine learning[edit]

Subfields of machine learning

Cross-disciplinary fields involving machine learning[edit]

Cross-disciplinary fields involving machine learning

Applications of machine learning[edit]

Applications of machine learning

Machine learning hardware[edit]

Machine learning hardware

Machine learning tools[edit]

Machine learning tools   (list)

Machine learning frameworks[edit]

Machine learning framework

Proprietary machine learning frameworks[edit]

Proprietary machine learning frameworks

Open source machine learning frameworks[edit]

Open source machine learning frameworks

Machine learning libraries[edit]

Machine learning library  

Machine learning algorithms[edit]

Machine learning algorithm

Types of machine learning algorithms[edit]

Machine learning methods[edit]

Machine learning method   (list)

Dimensionality reduction[edit]

Dimensionality reduction

Ensemble learning[edit]

Ensemble learning

Meta learning[edit]

Meta learning

Reinforcement learning[edit]

Reinforcement learning

Supervised learning[edit]

Supervised learning


Bayesian statistics

Decision tree algorithms[edit]

Decision tree algorithm

Linear classifier[edit]

Linear classifier

Unsupervised learning[edit]

Unsupervised learning

Artificial neural networks[edit]

Artificial neural network

Association rule learning[edit]

Association rule learning

Hierarchical clustering[edit]

Hierarchical clustering

Cluster analysis[edit]

Cluster analysis

Anomaly detection[edit]

Anomaly detection

Semi-supervised learning[edit]

Semi-supervised learning

Deep learning[edit]

Deep learning

Other machine learning methods and problems[edit]

Machine learning research[edit]

Machine learning research

History of machine learning[edit]

History of machine learning

Machine learning projects[edit]

Machine learning projects

Machine learning organizations[edit]

Machine learning organizations

Machine learning conferences and workshops[edit]

Machine learning publications[edit]

Books on machine learning[edit]

Books about machine learning

Machine learning journals[edit]

Persons influential in machine learning[edit]

See also[edit]


Further reading[edit]


  1. ^ a b  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. ^
  5. ^ Settles, Burr (2010), "Active Learning Literature Survey" (PDF), Computer Sciences Technical Report 1648. University of Wisconsin–Madison, retrieved 2014-11-18 
  6. ^ Rubens, Neil; Elahi, Mehdi; Sugiyama, Masashi; Kaplan, Dain (2016). "Active Learning in Recommender Systems". In Ricci, Francesco; Rokach, Lior; Shapira, Bracha. Recommender Systems Handbook (2 ed.). Springer US. doi:10.1007/978-1-4899-7637-6. ISBN 978-1-4899-7637-6. 

External links[edit]


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