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Outline of machine learning

From Wikipedia, the free encyclopedia

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

Machine learning – a subfield of soft computing within computer science 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 involves the study and construction of algorithms that can learn from and make predictions on data.[3] These algorithms operate by building a model from an example training set of input observations to make data-driven predictions or decisions expressed as outputs, rather than following strictly static program instructions.


What type of thing is machine learning?

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Paradigms of machine learning

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Applications of machine learning

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Machine learning hardware

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Machine learning tools

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Machine learning frameworks

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Proprietary machine learning frameworks

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Open source machine learning frameworks

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Machine learning libraries

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Machine learning algorithms

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Machine learning methods

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Instance-based algorithm

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Dimensionality reduction

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Dimensionality reduction

Ensemble learning

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Ensemble learning

Meta-learning

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Meta-learning

Reinforcement learning

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Reinforcement learning

Supervised learning

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Supervised learning

Bayesian

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Bayesian statistics

Decision tree algorithms

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Decision tree algorithm

Linear classifier

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Linear classifier

Unsupervised learning

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Unsupervised learning

Artificial neural networks

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Artificial neural network

Association rule learning

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Association rule learning

Hierarchical clustering

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Hierarchical clustering

Cluster analysis

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Cluster analysis

Anomaly detection

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Anomaly detection

Semi-supervised learning

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Semi-supervised learning

Deep learning

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Deep learning

Other machine learning methods and problems

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Machine learning research

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History of machine learning

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History of machine learning

Machine learning projects

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Machine learning projects

Machine learning organizations

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Machine learning organizations

Machine learning conferences and workshops

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Machine learning publications

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Books on machine learning

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Machine learning journals

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Persons influential in machine learning

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See also

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Other

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Further reading

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  • Trevor Hastie, Robert Tibshirani and Jerome H. Friedman (2001). The Elements of Statistical Learning, Springer. ISBN 0-387-95284-5.
  • Pedro Domingos (September 2015), The Master Algorithm, Basic Books, ISBN 978-0-465-06570-7
  • Mehryar Mohri, Afshin Rostamizadeh, Ameet Talwalkar (2012). Foundations of Machine Learning, The MIT Press. ISBN 978-0-262-01825-8.
  • Ian H. Witten and Eibe Frank (2011). Data Mining: Practical machine learning tools and techniques Morgan Kaufmann, 664pp., ISBN 978-0-12-374856-0.
  • David J. C. MacKay. Information Theory, Inference, and Learning Algorithms Cambridge: Cambridge University Press, 2003. ISBN 0-521-64298-1
  • Richard O. Duda, Peter E. Hart, David G. Stork (2001) Pattern classification (2nd edition), Wiley, New York, ISBN 0-471-05669-3.
  • Christopher Bishop (1995). Neural Networks for Pattern Recognition, Oxford University Press. ISBN 0-19-853864-2.
  • Vladimir Vapnik (1998). Statistical Learning Theory. Wiley-Interscience, ISBN 0-471-03003-1.
  • Ray Solomonoff, An Inductive Inference Machine, IRE Convention Record, Section on Information Theory, Part 2, pp., 56–62, 1957.
  • Ray Solomonoff, "An Inductive Inference Machine" A privately circulated report from the 1956 Dartmouth Summer Research Conference on AI.

References

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  1. ^ 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. doi:10.1023/A:1007411609915.
  4. ^ Settles, Burr (2010), "Active Learning Literature Survey" (PDF), Computer Sciences Technical Report 1648. University of Wisconsin–Madison, retrieved 2014-11-18
  5. ^ Rubens, Neil; Elahi, Mehdi; Sugiyama, Masashi; Kaplan, Dain (2016). "Active Learning in Recommender Systems". In Ricci, Francesco; Rokach, Lior; Shapira, Bracha (eds.). Recommender Systems Handbook (2 ed.). Springer US. doi:10.1007/978-1-4899-7637-6. hdl:11311/1006123. ISBN 978-1-4899-7637-6. S2CID 11569603.
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