机器学习简介课件

MachineLearning：

AnOverview石立臣-OutlineWhatismachinelearning(ML)TypesofmachinelearningWorkflowPopularmodelsApplicationsFutures-WhatismachinelearningTrainingset(labelsknown)Testset(labelsunknown)f()=“apple”f()=“tomato”f()=“cow”-WhatismachinelearningDefinitionMachinelearningreferstoasystemcapableoftheautonomousacquisitionandintegrationofknowledgeMachinelearningisprogrammingcomputerstooptimizeaperformancecriterionusingexampledataorpastexperienceComputerDataAlgorithmProgramKnowledgeKnowledge(new)-WhatismachinelearningEverymachinelearningalgorithmhasthreecomponentsRepresentationModel(rules,statistics,instance;logic,KNN,SVM,DNN,…)EvaluationPerformance(accuracy,mse,energy,entropy,…)OptimizationParametersCombinatorialoptimizationConvexoptimizationConstrainedoptimization-TypesofmachinelearningSupervisedlearningTrainingdataincludesdesiredoutputsUnsupervisedlearningTrainingdatadoesnotincludedesiredoutputsSemi-supervisedlearningTrainingdataincludesafewdesiredoutputsReinforcementlearningRewardsfromsequenceofactions-TypesofmachinelearningSupervisedlearningClassification:discreteoutputRegression:continuousoutputBias-variance-TrainingandValidationDataFullDataSetTrainingDataValidationDataIdea:traineachmodelonthe“trainingdata”andthentesteachmodel’saccuracyonthevalidationdata-Underfitting&OverfittingPredictiveErrorModelComplexityErroronTrainingDataErroronTestDataIdealRangeforModelComplexityOverfittingUnderfitting-TypesofmachinelearningUnsupervisedlearningClusteringDimensionalityreductionFactoranalysis-TypesofmachinelearningSemi-supervisedlearningClusteringorclassification-TypesofmachinelearningReinforcementlearningRobot&control-WorkflowPredictionTrainingLabelsTrainingTrainingImageFeaturesImageFeaturesTestingTestImageLearnedmodelLearnedmodelSlidecredit:D.HoiemandL.Lazebnik-WorkflowFeatures-WorkflowModelsLogic,RulesStatistical,BlackboxmodelStatic,dynamicmodelOnlinelearningEnsemblelearning-WorkflowArchitectureModelFeatureHardware-PopularmodelsLinearmodel:logisticregression,lineardiscriminantanalysis,linearregression(withbasisfunction)-PopularmodelsNearestneighborFeature&distance-PopularmodelsSupportvectormachine-PopularmodelsArtificialneuralnetwork-PopularmodelsDecisiontree-PopularmodelsCollaborativefiltering-PopularmodelsHierarchicalclusteringK-meansSpectralclusteringManifoldlearning-PopularmodelsHiddenmarkovmodelConditionalrandomfields-Applications-Applications-Applications-Applications-Applications-Applications-Applications-Applications-ApplicationsAttention-ApplicationsImageclassification-Applications-ApplicationsBrainmachineinterface-Applications-Applications-Applications-Applications-ApplicationsIndirectilluminationRegression-Applications