Outline of machine learning The following outline is provided as an overview of and topical guide to machine learning: Machine learning – subfield of computer science [1] (more particularly soft computing) 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? Branches of machine learning Subfields of machine learning Cross-disciplinary fields involving machine learning Applications of machine learning Machine learning hardware Machine learning tools Machine learning frameworks Machine learning libraries Machine learning algorithms Machine learning methods Dimensionality reduction Ensemble learning Meta learning Reinforcement learning Supervised learning Unsupervised learning Semi-supervised learning Deep learning Other machine learning methods and problems Machine learning research History of machine learning Machine learning projects Machine learning organizations Machine learning conferences and workshops Machine learning publications Books on machine learning Machine learning journals Persons influential in machine learning See also Other Further reading References External links Contents
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Outline of machine learningThe following outline is provided as an overview of and topical guide to machine learning:
Machine learning – subfield of computer science[1] (more particularly soft computing) that evolved from the study of patternrecognition 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 studyand construction of algorithms that can learn from and make predictions on data.[3] Such algorithms operate by building a modelfrom an example training set of input observations in order to make data-driven predictions or decisions expressed as outputs, ratherthan following strictly static program instructions.
What type of thing is machine learning?
Branches of machine learningSubfields of machine learningCross-disciplinary fields involving machine learning
AdaBoostBoostingBootstrap aggregating (Bagging)Ensemble averaging – process of creating multiple models and combining them to produce a desired output, asopposed 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 (GBRT)Gradient boosting machine (GBM)Random ForestStacked Generalization (blending)
Case-based reasoningGaussian process regressionGene expression programmingGroup method of data handling (GMDH)Inductive logic programmingInstance-based learningLazy learningLearning AutomataLearning Vector QuantizationLogistic Model TreeMinimum message length (decision trees, decision graphs, etc.)
Nearest Neighbor AlgorithmAnalogical modeling
Probably approximately correct learning (PAC) learningRipple down rules, a knowledge acquisition methodologySymbolic machine learning algorithmsSupport vector machinesRandom ForestsEnsembles of classifiers
Active learning – special case of semi-supervised learning in which a learning algorithm is able to interactively querythe user (or some other information source) to obtain the desired outputs at new data points.[5] [6]
International Conference on Machine Learning (ICML)
Books about machine learning
Machine LearningJournal of Machine Learning Research (JMLR)Neural Computation
Alberto BroggiAndrei KnyazevAndrew McCallumAndrew NgArmin B. CremersAyanna HowardBarney PellBen GoertzelBen TaskarBernhard SchölkopfBrian D. RipleyChristopher G. AtkesonCorinna CortesDemis HassabisDouglas LenatEric XingErnst DickmannsGeoffrey Hinton – co-inventor of the backpropagation and contrastive divergence training algorithmsHans-Peter KriegelHartmut NevenHeikki MannilaJacek M. ZuradaJaime CarbonellJerome H. FriedmanJohn D. LaffertyJohn Platt – invented SMO and Platt scalingJulie Beth LovinsJürgen SchmidhuberKarl SteinbuchKatia SycaraLeo Breiman – invented bagging and random forestsLise GetoorLuca Maria GambardellaLéon BottouMarcus HutterMehryar MohriMichael Collins
Information gain in decision treesInformation gain ratioInheritance (genetic algorithm)Instance selectionIntel RealSenseInteracting particle systemInteractive machine translationInternational Joint Conference on Artificial IntelligenceInternational Meeting on Computational Intelligence Methods for Bioinformatics and BiostatisticsInternational Semantic Web ConferenceIris flower data setIsland algorithmIsotropic positionItem response theoryIterative Viterbi decodingJOONEJabberwackyJaccard indexJackknife variance estimates for random forestJava Grammatical EvolutionJoseph NechvatalJubatusJulia (programming language)Junction tree algorithmK-SVDK-means++K-medians clusteringK-medoidsKNIMEKXEN Inc.K q-flatsKaggleKalman filterKatz's back-off modelKerasKernel adaptive filterKernel density estimationKernel eigenvoiceKernel embedding of distributionsKernel methodKernel perceptronKernel random forestKinectKlaus-Robert MüllerKneser–Ney smoothingKnowledge VaultKnowledge integrationLIBSVMLPBoostLabeled dataLanguageWareLanguage Acquisition Device (computer)Language identification in the limitLanguage modelLarge margin nearest neighbor
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 978-0-465-06570-7Mehryar 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 MorganKaufmann, 664pp., ISBN 978-0-12-374856-0.David J. C. MacKay. Information Theory, Inference, and Learning Algorithms Cambridge: Cambridge UniversityPress, 2003. ISBN 0-521-64298-1Richard 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 SummerResearch Conference on AI.
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 (https://books.google.com/books?id=Dn-Gdoh66sgC&pg=PA89#v=onepage&q&f=false). Wiley. p. 89. ISBN 978-1-118-63817-0.
3. Ron Kohavi; Foster Provost (1998). "Glossary of terms" (http://ai.stanford.edu/~ronnyk/glossary.html). MachineLearning. 30: 271–274.
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 StanfordUniversity'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.
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