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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.

Contents

What type of thing is machine learning?Edit

Branches of machine learningEdit

Subfields of machine learningEdit

Subfields of machine learning

Cross-disciplinary fields involving machine learningEdit

Cross-disciplinary fields involving machine learning

Applications of machine learningEdit

Machine learning hardwareEdit

Machine learning toolsEdit

Machine learning tools   (list)

Machine learning frameworksEdit

Machine learning framework

Proprietary machine learning frameworksEdit

Proprietary machine learning frameworks

Open source machine learning frameworksEdit

Open source machine learning frameworks

Machine learning librariesEdit

Machine learning library  

Machine learning algorithmsEdit

Machine learning algorithm

Types of machine learning algorithmsEdit

Machine learning methodsEdit

Machine learning method   (list)

Dimensionality reductionEdit

Dimensionality reduction

Ensemble learningEdit

Ensemble learning

Meta learningEdit

Meta learning

Reinforcement learningEdit

Reinforcement learning

Supervised learningEdit

Supervised learning

BayesianEdit

Bayesian statistics

Decision tree algorithmsEdit

Decision tree algorithm

Linear classifierEdit

Linear classifier

Unsupervised learningEdit

Unsupervised learning

Artificial neural networksEdit

Artificial neural network

Association rule learningEdit

Association rule learning

Hierarchical clusteringEdit

Hierarchical clustering

Cluster analysisEdit

Cluster analysis

Anomaly detectionEdit

Anomaly detection

Semi-supervised learningEdit

Semi-supervised learning

Deep learningEdit

Deep learning

Other machine learning methods and problemsEdit

Machine learning researchEdit

History of machine learningEdit

Machine learning projectsEdit

Machine learning projects

Machine learning organizationsEdit

Machine learning organizations

Machine learning conferences and workshopsEdit

Machine learning publicationsEdit

Books on machine learningEdit

Books about machine learning

Machine learning journalsEdit

Persons influential in machine learningEdit

See alsoEdit

OtherEdit

Further readingEdit

  • 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.

ReferencesEdit

  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.
  4. ^ http://www.learningtheory.org/
  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. ISBN 978-1-4899-7637-6.
  7. ^ https://en.wikipedia.org/wiki/Generative_adversarial_network#cite_note-GANs-1

External linksEdit