Outline of machine learning - Basic Knowledge...
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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
Transcript of Outline of machine learning - Basic Knowledge...
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
Applications of machine learning
Machine learning hardware
Machine learning toolsMachine learning frameworksMachine learning librariesMachine learning algorithms
Machine learning methodsDimensionality reductionEnsemble learningMeta learningReinforcement learningSupervised learningUnsupervised learningSemi-supervised learningDeep learningOther machine learning methods and problems
Machine learning research
History of machine learning
Machine learning projects
Machine learning organizationsMachine learning conferences and workshops
Machine learning publicationsBooks on machine learningMachine learning journals
Persons influential in machine learning
See alsoOther
Further reading
References
External links
Contents
An academic disciplineA branch of science
An applied science
A subfield of computer science
A branch of artificial intelligenceA subfield of soft computing
Subfields of machine learning
Computational learning theory – studying the design and analysis of machine learning algorithms.[4]
Grammar inductionMeta learning
Cross-disciplinary fields involving machine learning
Adversarial machine learningPredictive analyticsQuantum machine learningRobot learning
Developmental robotics
Applications of machine learning
Biomedical informaticsComputer visionCustomer relationship management –Data miningEmail filteringInverted pendulum – balance and equilibrium system.Natural language processing (NLP)
Automatic summarizationAutomatic taxonomy constructionDialog systemGrammar checkerLanguage recognition
Handwriting recognitionOptical character recognitionSpeech recognition
Machine translation
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 translationQuestion answeringSpeech synthesisText mining
Term frequency–inverse document frequency (tf–idf)
Text simplification
Pattern recognition
Facial recognition systemHandwriting recognitionImage recognitionOptical character recognitionSpeech recognition
Recommendation system
Collaborative filteringContent-based filteringHybrid recommender systems (Collaborative and content-based filtering)
Search engine
Search engine optimization
Machine learning hardware
Graphics processing unitTensor processing unitVision processing unit
Machine learning tools (list)
Comparison of deep learning software
Comparison of deep learning software/Resources
Machine learning framework
Proprietary machine learning frameworks
Amazon Machine LearningMicrosoft Azure Machine Learning StudioDistBelief – replaced by TensorFlowMicrosoft Cognitive Toolkit
Open source machine learning frameworks
Apache SingaCaffe
Machine learning hardware
Machine learning tools
Machine learning frameworks
Proprietary machine learning frameworks
Open source machine learning frameworks
H2OPyTorchMLPACKTensorFlowTorchAccord.Net
Machine learning library (list)
Deeplearning4jTheanoScikit-learn
Machine learning algorithm
Almeida–Pineda recurrent backpropagationALOPEXBackpropagationBootstrap aggregatingCN2 algorithmConstructing skill treesDehaene–Changeux modelDiffusion mapDominance-based rough set approachDynamic time warpingError-driven learningEvolutionary multimodal optimizationExpectation–maximization algorithmFastICAForward–backward algorithmGeneRecGenetic Algorithm for Rule Set ProductionGrowing self-organizing mapHEXQHyper basis function networkIDistanceK-nearest neighbors algorithmKernel methods for vector outputKernel principal component analysisLeabraLinde–Buzo–Gray algorithmLocal outlier factorLogic learning machineLogitBoostManifold alignmentMinimum redundancy feature selectionMixture of expertsMultiple kernel learning
Machine learning libraries
Machine learning algorithms
Types of machine learning algorithms
Non-negative matrix factorizationOnline machine learningOut-of-bag errorPrefrontal cortex basal ganglia working memoryPVLVQ-learningQuadratic unconstrained binary optimizationQuery-level featureQuickpropRadial basis function networkRandomized weighted majority algorithmReinforcement learningRepeated incremental pruning to produce error reduction (RIPPER)RpropRule-based machine learningSkill chainingSparse PCAState–action–reward–state–actionStochastic gradient descentStructured kNNT-distributed stochastic neighbor embeddingTemporal difference learningWake-sleep algorithmWeighted majority algorithm (machine learning)
Machine learning method (list)
Instance-based algorithm
K-nearest neighbors algorithm (KNN)Learning vector quantization (LVQ)Self-organizing map (SOM)
Regression analysis
Logistic regressionOrdinary least squares regression (OLSR)Linear regressionStepwise regressionMultivariate adaptive regression splines (MARS)
Regularization algorithm
Ridge regressionLeast Absolute Shrinkage and Selection Operator (LASSO)Elastic netLeast-angle regression (LARS)
Classifiers
Probabilistic classifier
Naive Bayes classifier
Binary classifierLinear classifierHierarchical classifier
Machine learning methods
Dimensionality reduction
Canonical correlation analysis (CCA)Factor analysisFeature extractionFeature selectionIndependent component analysis (ICA)Linear discriminant analysis (LDA)Multidimensional scaling (MDS)Non-negative matrix factorization (NMF)Partial least squares regression (PLSR)Principal component analysis (PCA)Principal component regression (PCR)Projection pursuitSammon mappingt-distributed stochastic neighbor embedding (t-SNE)
Ensemble 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)
Meta learning
Inductive biasMetadata
Reinforcement learning
Q-learningState–action–reward–state–action (SARSA)Temporal difference learning (TD)Learning Automata
Supervised learning
AODEArtificial neural network
Dimensionality reduction
Ensemble learning
Meta learning
Reinforcement learning
Supervised learning
Association rule learning algorithms
Apriori algorithmEclat algorithm
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
Bootstrap aggregating (bagging)Boosting (meta-algorithm)
Ordinal classificationInformation fuzzy networks (IFN)Conditional Random FieldANOVAQuadratic classifiersk-nearest neighborBoosting
SPRINT
Bayesian networks
Naive Bayes
Hidden Markov models
Hierarchical hidden Markov model
Bayesian statistics
Bayesian knowledge baseNaive BayesGaussian Naive BayesMultinomial Naive BayesAveraged One-Dependence Estimators (AODE)Bayesian Belief Network (BBN)Bayesian Network (BN)
Decision tree algorithm
Bayesian
Decision tree algorithms
Decision treeClassification and regression tree (CART)Iterative Dichotomiser 3 (ID3)C4.5 algorithmC5.0 algorithmChi-squared Automatic Interaction Detection (CHAID)Decision stumpConditional decision treeID3 algorithmRandom forestSLIQ
Linear classifier
Fisher's linear discriminantLinear regressionLogistic regressionMultinomial logistic regressionNaive Bayes classifierPerceptronSupport vector machine
Unsupervised learning
Expectation-maximization algorithmVector QuantizationGenerative topographic mapInformation bottleneck method
Artificial neural network
Feedforward neural network
Extreme learning machine
Logic learning machineSelf-organizing map
Association rule learning
Apriori algorithmEclat algorithmFP-growth algorithm
Hierarchical clustering
Single-linkage clustering
Linear classifier
Unsupervised learning
Artificial neural networks
Association rule learning
Hierarchical clustering
Conceptual clustering
Cluster analysis
BIRCHDBSCANExpectation-maximization (EM)Fuzzy clusteringHierarchical ClusteringK-means algorithmK-means clusteringK-mediansMean-shiftOPTICS algorithm
Anomaly detection
k-nearest neighbors classification (k-NN)Local outlier factor
Semi-supervised learning
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]
Generative modelsLow-density separationGraph-based methodsCo-trainingTransduction
Deep learning
Deep belief networksDeep Boltzmann machinesDeep Convolutional neural networksDeep Recurrent neural networksHierarchical temporal memoryDeep Boltzmann Machine (DBM)Stacked Auto-Encoders
Anomaly detectionAssociation rulesBias-variance dilemmaClassification
Cluster analysis
Anomaly detection
Semi-supervised learning
Deep learning
Other machine learning methods and problems
Multi-label classification
ClusteringData Pre-processingEmpirical risk minimizationFeature engineeringFeature learningLearning to rankOccam learningOnline machine learningPAC learningRegressionReinforcement LearningSemi-supervised learningStatistical learningStructured prediction
Graphical models
Bayesian networkConditional random field (CRF)Hidden Markov model (HMM)
Unsupervised learningVC theory
Machine learning research
List of artificial intelligence projectsList of datasets for machine learning research
History of machine learning
Timeline of machine learning
Machine learning projects
DeepMindGoogle Brain
Machine learning organizations
Knowledge Engineering and Machine Learning Group
Artificial Intelligence and Security (AISec) (co-located workshop with CCS)Conference on Neural Information Processing Systems (NIPS)ECML PKDD
Machine learning research
History of machine learning
Machine learning projects
Machine learning organizations
Machine learning conferences and workshops
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
Machine learning publications
Books on machine learning
Machine learning journals
Persons influential in machine learning
Michael I. JordanMichael L. LittmanNando de FreitasOfer DekelOren EtzioniPedro DomingosPeter FlachPierre BaldiPushmeet KohliRay KurzweilRayid GhaniRoss QuinlanSalvatore J. StolfoSebastian ThrunSelmer BringsjordSepp HochreiterShane LeggStephen MuggletonSteve OmohundroTom M. MitchellTrevor HastieVasant HonavarVladimir Vapnik – co-inventor of the SVM and VC theoryYann LeCun – invented convolutional neural networksYasuo MatsuyamaYoshua BengioZoubin Ghahramani
Outline of artificial intelligence
Outline of computer visionOutline of natural language processing
Outline of roboticsAccuracy paradoxAction model learningActivation functionActivity recognitionADALINEAdaptive neuro fuzzy inference systemAdaptive resonance theoryAdditive smoothingAdjusted mutual informationAika (software)AIVAAIXIAlchemyAPIAlexNetAlgorithm selectionAlgorithmic inferenceAlgorithmic learning theoryAlphaGoAlphaGo ZeroAlternating decision tree
See also
Apprenticeship learningCausal Markov conditionCompetitive learningConcept learningDecision tree learningDistribution learning theoryEager learningEnd-to-end reinforcement learningError tolerance (PAC learning)Explanation-based learningFeatureGloVeHyperparameterIBM Machine Learning HubInferential theory of learningLearning automataLearning classifier systemLearning ruleLearning with errorsM-Theory (learning framework)Machine learning controlMachine learning in bioinformaticsMarginMarkov chain geostatisticsMarkov chain Monte Carlo (MCMC)Markov information sourceMarkov logic networkMarkov modelMarkov random fieldMarkovian discriminationMaximum-entropy Markov modelMulti-armed banditMulti-task learningMultilinear subspace learningMultimodal learningMultiple instance learningMultiple-instance learningNever-Ending Language LearningOffline learningParity learningPopulation-based incremental learningPredictive learningPreference learningProactive learningProximal gradient methods for learningSemantic analysisSimilarity learningSparse dictionary learningStability (learning theory)Statistical learning theoryStatistical relational learningTanagraTransfer learningVariable-order Markov modelVersion space learning
WafflesWeka
Loss function
Loss functions for classificationMean squared error (MSE)Mean squared prediction error (MSPE)Taguchi loss function
Low-energy adaptive clustering hierarchy
Anne O'TateAnt colony optimization algorithmsAnthony LevandowskiAnti-unification (computer science)Apache FlumeApache GiraphApache MahoutApache SINGAApache SparkApache SystemMLAphelion (software)Arabic Speech CorpusArchetypal analysisArthur ZimekArtificial antsArtificial bee colony algorithmArtificial developmentArtificial immune systemAstrostatisticsAveraged one-dependence estimatorsBag-of-words modelBalanced clusteringBall treeBase rateBat algorithmBaum–Welch algorithmBayesian hierarchical modelingBayesian interpretation of kernel regularizationBayesian optimizationBayesian structural time seriesBees algorithmBehavioral clusteringBernoulli schemeBias–variance tradeoffBiclusteringBinarization of consensus partition matricesBinary classificationBing PredictsBio-inspired computingBiogeography-based optimizationBiplotBondy's theorem
Other
Bongard problemBradley–Terry modelBrownBoostBrown clusteringBurst errorCBCL (MIT)CIML community portalCMA-ESCURE data clustering algorithmCache language modelCalibration (statistics)Canonical correspondence analysisCanopy clustering algorithmCascading classifiersCategory utilityCellCognitionCellular evolutionary algorithmChi-square automatic interaction detectionChromosome (genetic algorithm)Classifier chainsCleverbotClonal selection algorithmCluster-weighted modelingClustering high-dimensional dataClustering illusionCoBoostingCobweb (clustering)Cognitive computerCognitive roboticsCollostructional analysisCommon-method varianceComplete-linkage clusteringComputer-automated designConcept classConcept driftConference on Artificial General IntelligenceConference on Knowledge Discovery and Data MiningConfirmatory factor analysisConfusion matrixCongruence coefficientConnect (computer system)Consensus clusteringConstrained clusteringConstrained conditional modelConstructive cooperative coevolutionCorrelation clusteringCorrespondence analysisCorticaCoupled pattern learnerCross-entropy methodCross-validation (statistics)Crossover (genetic algorithm)Cuckoo searchCultural algorithmCultural consensus theory
Curse of dimensionalityDADiSPDARPA LAGR ProgramDarkforestDartmouth workshopDarwinTunesData Mining ExtensionsData explorationData pre-processingData stream clusteringDataikuDavies–Bouldin indexDecision boundaryDecision listDecision tree modelDeductive classifierDeepArtDeepDreamDeep Web TechnologiesDefining lengthDendrogramDependability state modelDetailed balanceDetermining the number of clusters in a data setDetrended correspondence analysisDevelopmental roboticsDiffbotDifferential evolutionDiscrete phase-type distributionDiscriminative modelDissociated pressDistributed RDlibDocument classificationDocumenting HateDomain adaptationDoubly stochastic modelDual-phase evolutionDunn indexDynamic Bayesian networkDynamic Markov compressionDynamic topic modelDynamic unobserved effects modelEDLUTELKIEdge recombination operatorEffective fitnessElastic mapElastic matchingElbow method (clustering)Emergent (software)EncogEntropy rateErkki OjaEurisko
European Conference on Artificial IntelligenceEvaluation of binary classifiersEvolution strategyEvolution windowEvolutionary Algorithm for Landmark DetectionEvolutionary algorithmEvolutionary artEvolutionary musicEvolutionary programmingEvolvability (computer science)Evolved antennaEvolver (software)Evolving classification functionExpectation propagationExploratory factor analysisF1 scoreFLAME clusteringFactor analysis of mixed dataFactor graphFactor regression modelFactored language modelFarthest-first traversalFast-and-frugal treesFeature Selection ToolboxFeature hashingFeature scalingFeature vectorFirefly algorithmFirst-difference estimatorFirst-order inductive learnerFish School SearchFisher kernelFitness approximationFitness functionFitness proportionate selectionFluentdFolding@homeFormal concept analysisForward algorithmFowlkes–Mallows indexFrederick JelinekFrroleFunctional principal component analysisGATTOGLIMMERGary Bryce FogelGaussian adaptationGaussian processGaussian process emulatorGene predictionGeneral Architecture for Text EngineeringGeneralization errorGeneralized canonical correlationGeneralized filteringGeneralized iterative scaling
Generalized multidimensional scalingGenerative adversarial networkGenerative modelGenetic algorithmGenetic algorithm schedulingGenetic algorithms in economicsGenetic fuzzy systemsGenetic memory (computer science)Genetic operatorGenetic programmingGenetic representationGeographical clusterGesture Description LanguageGeworkbenchGlossary of artificial intelligenceGlottochronologyGolem (ILP)Google matrixGrafting (decision trees)Gramian matrixGrammatical evolutionGranular computingGraphLabGraph kernelGremlin (programming language)Growth functionHUMANT (HUManoid ANT) algorithmHammersley–Clifford theoremHarmony searchHebbian theoryHidden Markov random fieldHidden semi-Markov modelHierarchical hidden Markov modelHigher-order factor analysisHighway networkHinge lossHolland's schema theoremHopkins statisticHoshen–Kopelman algorithmHuber lossIRCF360Ian GoodfellowIlastikIlya SutskeverImmunocomputingImperialist competitive algorithmInauthentic textIncremental decision treeInduction of regular languagesInductive biasInductive probabilityInductive programmingInfluence diagramInformation HarvestingInformation fuzzy networks
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
Latent Dirichlet allocationLatent class modelLatent semantic analysisLatent variableLatent variable modelLattice MinerLayered hidden Markov modelLearnable function classLeast squares support vector machineLeave-one-out errorLeslie P. KaelblingLinear genetic programmingLinear predictor functionLinear separabilityLingyun GuLinkuriousLior Ron (business executive)List of genetic algorithm applicationsList of metaphor-based metaheuristicsList of text mining softwareLocal case-control samplingLocal independenceLocal tangent space alignmentLocality-sensitive hashingLog-linear modelLogistic model treeLow-rank approximationLow-rank matrix approximationsMATLABMIMIC (immunology)MXNetMallet (software project)Manifold regularizationMargin-infused relaxed algorithmMargin classifierMark V. ShaneyMassive Online AnalysisMatrix regularizationMatthews correlation coefficientMean shiftMean squared errorMean squared prediction errorMeasurement invarianceMedoidMeeMixMelomicsMemetic algorithmMeta-optimizationMexican International Conference on Artificial IntelligenceMichael Kearns (computer scientist)MinHashMixture modelMlpyModels of DNA evolutionMoral graph
Mountain car problemMovidiusMulti-armed banditMulti-label classificationMulti expression programmingMulticlass classificationMultidimensional analysisMultifactor dimensionality reductionMultilinear principal component analysisMultiple correspondence analysisMultiple discriminant analysisMultiple factor analysisMultiple sequence alignmentMultiplicative weight update methodMultispectral pattern recognitionMutation (genetic algorithm)MysteryVibeN-gramNOMINATE (scaling method)Native-language identificationNatural Language ToolkitNatural evolution strategyNearest-neighbor chain algorithmNearest centroid classifierNearest neighbor searchNeighbor joiningNest LabsNetMinerNetOwlNeural DesignerNeural Engineering ObjectNeural LabNeural modeling fieldsNeural network softwareNeuroSolutionsNeuro LaboratoryNeuroevolutionNeurophNiki.aiNoisy channel modelNoisy text analyticsNonlinear dimensionality reductionNovelty detectionNuisance variableNumentaOne-class classificationOnnxOpenNLPOptimal discriminant analysisOracle Data MiningOrange (software)Ordination (statistics)OverfittingPROGOLPSIPRED
Pachinko allocationPageRankParallel metaheuristicParity benchmarkPart-of-speech taggingParticle swarm optimizationPath dependencePattern language (formal languages)Peltarion SynapsePerplexityPersian Speech CorpusPicas (app)Pietro PeronaPipeline PilotPiranha (software)Pitman–Yor processPlate notationPolynomial kernelPop music automationPopulation processPortable Format for AnalyticsPredictive Model Markup LanguagePredictive state representationPreference regressionPremature convergencePrincipal geodesic analysisPrior knowledge for pattern recognitionPrisma (app)Probabilistic Action CoresProbabilistic context-free grammarProbabilistic latent semantic analysisProbabilistic soft logicProbability matchingProbit modelProduct of expertsProgramming with Big Data in RProper generalized decompositionPruning (decision trees)Pushpak BhattacharyyaQ methodologyQlooQuality control and genetic algorithmsQuantum Artificial Intelligence LabQueueing theoryQuick, Draw!R (programming language)Rada MihalceaRademacher complexityRadial basis function kernelRand indexRandom indexingRandom projectionRandom subspace methodRanking SVMRapidMiner
Rattle GUIRaymond CattellReasoning systemRegularization perspectives on support vector machinesRelational data miningRelationship squareRelevance vector machineRelief (feature selection)RenjinRepertory gridRepresenter theoremReward-based selectionRichard ZemelRight to explanationRoboEarthRobust principal component analysisRuleML SymposiumRule inductionRules extraction system familySAS (software)SNNSSPSS ModelerSUBCLUSample complexitySample exclusion dimensionSanta Fe Trail problemSavi TechnologySchema (genetic algorithms)Search-based software engineeringSelection (genetic algorithm)Self-Service Semantic SuiteSemantic foldingSemantic mapping (statistics)Semidefinite embeddingSense NetworksSensorium ProjectSequence labelingSequential minimal optimizationShattered setShogun (toolbox)Silhouette (clustering)SimHashSimRankSimilarity measureSimple matching coefficientSimultaneous localization and mappingSinkov statisticSliced inverse regressionSmartMatchSnakes and LaddersSoft independent modelling of class analogiesSoft output Viterbi algorithmSolomonoff's theory of inductive inferenceSolveIT SoftwareSpectral clustering
Spike-and-slab variable selectionStatistical machine translationStatistical parsingStatistical semanticsStefano SoattoStephen WolframStochastic block modelStochastic cellular automatonStochastic diffusion searchStochastic grammarStochastic matrixStochastic universal samplingStress majorizationString kernelStructural equation modelingStructural risk minimizationStructured sparsity regularizationStructured support vector machineSubclass reachabilitySufficient dimension reductionSukhotin's algorithmSum of absolute differencesSum of absolute transformed differencesSwarm intelligenceSwitching Kalman filterSymbolic regressionSynchronous context-free grammarSyntactic pattern recognitionTD-GammonTIMITTeaching dimensionTeuvo KohonenTextual case-based reasoningTheory of conjoint measurementThomas G. DietterichThurstonian modelTopic modelTournament selectionTraining, test, and validation setsTransiogramTrax Image RecognitionTrigram taggerTruncation selectionTucker decompositionUIMAUPGMAUgly duckling theoremUncertain dataUniform convergence in probabilityUnique negative dimensionUniversal portfolio algorithmUser behavior analyticsVC dimensionVGG Image AnnotatorVIGRA
Validation setVapnik–Chervonenkis theoryVariable-order Bayesian networkVariable kernel density estimationVariable rules analysisVariational message passingVarimax rotationVector quantizationVicarious (company)Viterbi algorithmVowpal WabbitWACA clustering algorithmWPGMAWard's methodWeasel programWhitening transformationWinnow (algorithm)Win–stay, lose–switchWitness setWolfram LanguageWolfram MathematicaWriter invariantXgboostYooreekaZeroth (software)
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.
4. http://www.learningtheory.org/
Further reading
References
5. Settles, Burr (2010), "Active Learning Literature Survey" (http://pages.cs.wisc.edu/~bsettles/pub/settles.activelearning.pdf) (PDF), Computer Sciences Technical Report 1648. University of Wisconsin–Madison, retrieved 2014-11-18
6. Rubens, Neil; Elahi (https://www.linkedin.com/in/mehdielahi), Mehdi; Sugiyama, Masashi; Kaplan, Dain (2016)."Active Learning in Recommender Systems" (https://rd.springer.com/chapter/10.1007/978-1-4899-7637-6_24). InRicci, Francesco; Rokach, Lior; Shapira, Bracha. Recommender Systems Handbook (https://rd.springer.com/book/10.1007/978-1-4899-7637-6) (2 ed.). Springer US. doi:10.1007/978-1-4899-7637-6 (https://doi.org/10.1007%2F978-1-4899-7637-6). ISBN 978-1-4899-7637-6.
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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