机器学习简介ppt课件

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