Wikipedia

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 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 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

Cross-disciplinary fields involving machine learning edit

Applications of machine learning edit

Machine learning hardware edit

Machine learning tools edit

Machine learning frameworks edit

Proprietary machine learning frameworks edit

Open source machine learning frameworks edit

Machine learning libraries edit

Machine learning algorithms edit

Machine learning methods edit

Instance-based algorithm edit

Regression analysis edit

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 edit

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

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

Machine learning journals edit

Persons influential in machine learning edit

See also edit

Other edit

Further reading edit

  • 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 edit

  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. ^ "ACL - Association for Computational Learning".
  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 (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.

External links edit

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