Outline of machine learning source: en.wikipedia.org/wiki/Outline_of_machine_learning

The following outline is provided as an overview of and topical guide to machine learning. Machine learning is 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 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[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]

Books about machine learning

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]