机器学习简介53208PPT课件

.,1,MachineLearning：AnOverview,石立臣,.,2,Outline,Whatismachinelearning(ML)TypesofmachinelearningWorkflowPopularmodelsApplicationsFutures,.,3,Whatismachinelearning,Trainingset(labelsknown),Testset(labelsunknown),f()=“apple”f()=“tomato”f()=“cow”,.,4,Whatismachinelearning,DefinitionMachinelearningreferstoasystemcapableoftheautonomousacquisitionandintegrationofknowledgeMachinelearningisprogrammingcomputerstooptimizeaperformancecriterionusingexampledataorpastexperience,Computer,Data,Algorithm,Program,Knowledge,Knowledge(new),.,5,Whatismachinelearning,EverymachinelearningalgorithmhasthreecomponentsRepresentationModel(rules,statistics,instance;logic,KNN,SVM,DNN,)EvaluationPerformance(accuracy,mse,energy,entropy,)OptimizationParametersCombinatorialoptimizationConvexoptimizationConstrainedoptimization,.,6,Typesofmachinelearning,SupervisedlearningTrainingdataincludesdesiredoutputsUnsupervisedlearningTrainingdatadoesnotincludedesiredoutputsSemi-supervisedlearningTrainingdataincludesafewdesiredoutputsReinforcementlearningRewardsfromsequenceofactions,.,7,Typesofmachinelearning,SupervisedlearningClassification:discreteoutputRegression:continuousoutput,Bias-variance,.,8,TrainingandValidationData,FullDataSet,TrainingData,ValidationData,Idea:traineachmodelonthe“trainingdata”andthentesteachmodelsaccuracyonthevalidationdata,.,9,Underfitting&Overfitting,PredictiveError,ModelComplexity,ErroronTrainingData,ErroronTestData,IdealRangeforModelComplexity,Overfitting,Underfitting,.,10,Typesofmachinelearning,UnsupervisedlearningClusteringDimensionalityreductionFactoranalysis,.,11,Typesofmachinelearning,Semi-supervisedlearningClusteringorclassification,.,12,Typesofmachinelearning,ReinforcementlearningRobot&control,.,13,Workflow,Prediction,TrainingLabels,Training,Training,ImageFeatures,ImageFeatures,Testing,TestImage,Learnedmodel,Learnedmodel,Slidecredit:D.HoiemandL.Lazebnik,.,14,Workflow,Features,.,15,Workflow,ModelsLogic,RulesStatistical,BlackboxmodelStatic,dynamicmodelOnlinelearningEnsemblelearning,.,16,Workflow,Architecture,Model,Feature,Hardware,.,17,Popularmodels,Linearmodel:logisticregression,lineardiscriminantanalysis,linearregression(withbasisfunction),.,18,Popularmodels,NearestneighborFeature&distance,.,19,Popularmodels,Supportvectormachine,.,20,Popularmodels,Artificialneuralnetwork,.,21,Popularmodels,Decisiontree,.,22,Popularmodels,Collaborativefiltering,.,23,Popularmodels,HierarchicalclusteringK-meansSpectralclusteringManifoldlearning,.,24,Popularmodels,HiddenmarkovmodelConditionalrandomfields,.,25,Applications,.,26,Applications,.,27,Applications,.,28,Applications,.,29,Applications,.,30,Applications,.,31,Applications,.,32,Applications,.,33,Applications,Attention,.,34,Applications,Imageclassification,.,35,Applications,.,36,Applications,Brainmachineinterface,.,37,Applications,.,38,Applications,.,39,Applications,.,40,Applications,.,41,Applications,IndirectilluminationRegression,.,42,Applica