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.

Part of a series on
Machine learning
and
data mining

What type of thing is machine learning?

Branches of machine learning

Subfields of machine learning

Subfields of machine learning

Cross-disciplinary fields involving machine learning

Cross-disciplinary fields involving machine learning

Applications of machine learning

Applications of machine learning

Machine learning hardware

Machine learning hardware

Machine learning tools

Machine learning tools   (list)

  • Comparison of deep learning software
    • Comparison of deep learning software/Resources

Machine learning frameworks

Machine learning framework

Proprietary machine learning frameworks

Proprietary machine learning frameworks

Open source machine learning frameworks

Open source machine learning frameworks

Machine learning libraries

Machine learning library  

Machine learning algorithms

Machine learning algorithm

Types of machine learning algorithms

Machine learning methods

Machine learning method   (list)

Dimensionality reduction

Dimensionality reduction

Ensemble learning

Ensemble learning

  • AdaBoost
  • Boosting
  • Bootstrap aggregating (Bagging)
  • Ensemble averaging – process of creating multiple models and combining them to produce a desired output, as opposed to creating just one model. Frequently an ensemble of models performs better than any individual model, because the various errors of the models "average out."
  • Gradient boosted decision tree (GBDT)
  • Gradient boosting machine (GBM)
  • Random Forest
  • Stacked Generalization (blending)

Meta learning

Meta learning

Reinforcement learning

Reinforcement learning

Supervised learning

Supervised learning

Bayesian

Bayesian statistics

  • Bayesian knowledge base
  • Naive Bayes
  • Gaussian Naive Bayes
  • Multinomial Naive Bayes
  • Averaged One-Dependence Estimators (AODE)
  • Bayesian Belief Network (BBN)
  • Bayesian Network (BN)

Decision tree algorithms

Decision tree algorithm

Linear classifier

Linear classifier

Unsupervised learning

Unsupervised learning

Artificial neural networks

Artificial neural network

Association rule learning

Association rule learning

Hierarchical clustering

Hierarchical clustering

Cluster analysis

Cluster analysis

Anomaly detection

Anomaly detection

Semi-supervised learning

Semi-supervised learning

Deep learning

Deep learning

Other machine learning methods and problems

Machine learning research

History of machine learning

History of machine learning

Machine learning projects

Machine learning projects

Machine learning organizations

Machine learning organizations

Machine learning conferences and workshops

Machine learning publications

Books on machine learning

Books about machine learning

Machine learning journals

Persons influential in machine learning
gollark: Just use minimax.
gollark: And it... isn't whitespacey, as far as I know.
gollark: I don't use bash significantly.
gollark: Lua's basically whitespace-insensitive. Very elegant.
gollark: Nope!

See also

Other


Further reading
  • 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
  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.
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