Overview of and topical guide to machine learning
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?
Paradigms of machine learning
Applications of machine learning
Machine learning hardware
Machine learning tools
Machine learning frameworks
Proprietary machine learning frameworks
Open source machine learning frameworks
Machine learning libraries
Machine learning algorithms
Machine learning methods
Instance-based algorithm
Regression analysis
Dimensionality reduction
Dimensionality reduction
Ensemble learning
Ensemble learning
Meta-learning
Meta-learning
Reinforcement learning
Reinforcement learning
Supervised learning
Supervised learning
Bayesian
Bayesian statistics
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
- Mathematics for Machine Learning
- Hands-On Machine Learning Scikit-Learn, Keras, and TensorFlow
- The Hundred-Page Machine Learning Book
Machine learning journals
Persons influential in machine learning
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
- ^ http://www.britannica.com/EBchecked/topic/1116194/machine-learning This tertiary source reuses information from other sources but does not name them.
- ^ Phil Simon (March 18, 2013). Too Big to Ignore: The Business Case for Big Data. Wiley. p. 89. ISBN 978-1-118-63817-0.
- ^ Ron Kohavi; Foster Provost (1998). "Glossary of terms". Machine Learning. 30: 271–274. doi:10.1023/A:1007411609915.
- ^ Settles, Burr (2010), "Active Learning Literature Survey" (PDF), Computer Sciences Technical Report 1648. University of Wisconsin–Madison, retrieved 2014-11-18
- ^ 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
- Data Science: Data to Insights from MIT (machine learning)
- Popular online course by Andrew Ng, at Coursera. It uses GNU Octave. The course is a free version of Stanford University's actual course taught by Ng, see.stanford.edu/Course/CS229 available for free].
- mloss is an academic database of open-source machine learning software.