数据挖掘课题fornewbie

机器学习研究入门指南浙江大学蔡登提纲一、需要掌握的先决知识、技能二、入门学习资料三、如何学习四、找自己感兴趣的问题,进一步深入

五、跟踪学术前沿，发表高水平论文PrerequisiteKnowledgeMathematicalanalysisLinearalgebraProbabilitytheoryBayestheoremEigenvectorandeigenvalueMatrixrank,Positivedefinitematrix……AlgorithmdesignC,C++入门学习资料：机器学习R.Duda,P.Hart&D.Stork,PatternClassification(2nded.),Wiley,2000KevinMurphy,MachineLearning:AProbabilisticPerspective,TheMITPress,2012C.M.Bishop,PatternRecognitionandMachineLearning,Springer,2006

T.Hastie,R.Tibshirani&J.Friedman,TheElementsofStatisticalLearning:DataMining,Inference,andPrediction(2nded.),Springer,2009

入门学习资料：OptimizationStephenBoyd,LievenVandenberghe,ConvexOptimization,CambridgeUniversityPress,2004

入门学习资料：具体应用ComputerVisionDavidForsyth,JeanPonce,ComputerVision:AModernApproach(2ndEdition),PrenticeHall,2011RichardSzeliski,ComputerVision:AlgorithmsandApplications,Springer,2010

InformationRetrievalChristopherD.Manninget.al.,IntroductiontoInformationRetrieval,CambridgeUniversityPress,2008

NaturalLanguageProcessingChristopherD.Manninget.al.,FoundationsofStatisticalNaturalLanguageProcessing,MITPress,1999

入门学习资料下载地址，百度网盘共享链接：密码：e3vi除了上面提到的书，还有许多CS方面的如何学习掌握机器学习基本概念看书国外很多课程（包括网上课程和非网上课程，要做assignments），要做assignments针对一个问题，深入学习机器学习中有很多问题，譬如Classification,Clustering,Dimensionalityreduction,Matrixfactorization,Featureselection,Metriclearning,Activelearning…一个问题，会有很多方法解决，选择一些经典的/最新的方法，下载标准数据集，做实验比较，分析实验结果，写report这个过程，就是做研究的过程，和真正做研究写论文的唯一区别就是这里没有你自己提出的方法。整个过程要按照写论文的标准要求自己最后report也要写成论文的格式。需要你自己去下载相关领域的论文，看别人是怎么写的。如何学习深入学习的过程中，需要掌握Matlab，做实验一般都需要，自己去google教程，看看别人写的代码，通过读别人代码，写自己代码进行学习有很多机器学习matlab代码掌握Latex写论文。如果你以后准备念Ph.D，那Latex就必须会。上一个slide提到的report，就要用latex写。找自己感兴趣的问题，进一步深入有了机器学习的基本知识/技能之后，可以结合实际问题，尝试解决实际问题，一开始，这个问题可以是很小的问题，甚至是可以别人已经解决的问题。解决实际问题，光有机器学习的只是不够，还需要有领域相关的只是，譬如计算机视觉、文本处理、自然语言处理等，所以还要看相关的书下面几页slide，列了一些问题实际问题计算机视觉(ComputerVision)相关ImageSearchDetection,tracking,recognitionFacelandmarkImagerecovery文本处理(InformationRetrieval)ObjectDetection(目标检测)What’sobjectdetection?OnewayofdetectionFaceClassifierDetectionbydoingslidingwindowsearchCutmanysmallimagesfrombigoneAppendingaclassifierforeachsmallimageGetdetectionresultTwoClassesofObjectDetectionSpecificObjectDetectionFaceDetectionPedestrianDetectionCarDetection…GeneralObjectDetectionPascalVOCImageNetSpecificObjectDetectionFaceDetection(Youcanfindpaperlinkinfddbwebsite)Faceness,ICCV2015,Best,TangXiaoou组

upsamplingfeatrue->rankingwindows->detectCassacdeCNN,CVPR2015,adobe

CNNCassacdeFromsmalltobigfiliterwindowsJointCascade,ECCV2014DetectionandalignmentinthesamecascadeframeworkHeadHunter,ECCV2014

Cascade,dividetrainofmodelsby23poses,thanjoinallMultiresHPM,CVPR2014Manageocclusionintrain.CCF,ICCV2015PertrainCNNtogenrentefeature->detectDenseBox

SpecificObjectDetectionFaceDetectionDatasetFDDB(FaceDetectionDataSetandBenchmark)2845imageswithatotalof5171faces

AFW207imageswith337face

AFLW25kannotatedfaces

Baiduyun:CelebA(CelebFacesAttributesDataset)202,599

numberof

faceimages

Baiduyun:NonefaceimagefornegativesamplePacalvocnonefaceclass

SpecificObjectDetectionPedestrianDetectionSurvey:TenYearsofPedestrianDetection,WhatHaveWeLearned

Pedestriandetectionat100framespersecond

Filteredchannelfeaturesforpedestriandetection

SpecificObjectDetectionPedestrianDetectionDatasetINRIA:ETH:TUD-Brussels:Daimler:Caltech-USA:KITTI'spage(car,pedestrian,etc.):SpecificObjectDetectionCarDetectionDenseBox

CarDetectionDatasetKITTI'spage(car,pedestrian,etc.):UIUC:GeneralObjectDetectionSurvey:Whatmakesforeffectivedetectionproposals?

EdgeBoxes:LocatingObjectProposalsfromEdges

FasterR-CNN

Dataset:Imagenet:

PASCALVOC:

SourceCode行人100帧的代码

IntegralChannelFeatures以及特征估计的提出者主页，是后来行人100帧的奠基，他们是用Matlab代码的。EdgeBoxes

Faster-RCNN

FaceLandmarkFaceLandmarksarealsocalledfacialfeaturepointswhichareoftenreferredtosomecriticalpointsofaface,suchaseyecorners,mouthcornersetc.ObjectiveGivenanimageDetectfacesintheimagePredictlandmarksofeachfaceObjectiveMethodsPopularMethods:Regression-basedModelsCoarse-to-fineModelsDeepLearningModelsTraditionalMethods:ConstrainedLocalModelActiveAppearanceModelRegression-basedModelsCVPR2014FaceAlignmentat3000fpsviaregressinglocalbinaryfeaturesCVPR2014OnemillisecondfacealignmentwithanensembleofregressiontreesCVPR2014RobustfacelandmarkestimationunderocclusionCVPR2015FaceAlignmentbyCoarse-to-fineshapesearchingCVPR2015workshop,TowardsrobustcascaderegressionforfacealignmentinthewildCVPR2015FaceAlignmentusingCascadeGaussianProcessRegressionTreesCVPR2015Project-OutCascadedRegressionwithanapplicationtoFaceAlignmentCoarse-to-fineModelsCVPR2013DeepConvolutionNetworkCascadeforFacialPointDetectionECCV2014Coarse-to-fineautoencodernetworksforreal-timefacealignmentCVPR2015FaceAlignmentbyCoarse-to-fineshapesearchingDeepLearningModelsCVPR2013DeepConvolutionNetworkCascadeforFacialPointDetectionECCV2014Coarse-to-fineautoencodernetworksforreal-timefacealignmentCVPR2014FacialLandmarkDetectionbyDeepLearningMulti-taskLearningTPAMI2015LearningDeepRepresentationforFaceAlignmentwithAuxiliaryAttributesAvailableCodesFaceAlignmentbyExplicitShapeRegression[C++]SupervisedDescentMethod(SDM)forFaceAlignment[Matlab]Facealignmentat3000fps[Matlab][C++]AvailableCodesOneMillisecondFaceAlignmentwithanEnsembleofRegressionTrees[C++]codeisavailableindlibC++library.FaceAlignmentbyCoarse-to-FineShapeSearching[Matlab]DeepConvolutionalNetworkCascadeforFacialPointDetection[Matlab]justexecutablefile,norealusefulcode,[Caffe]CLandmark(comesfromflandmark)[C++]FacialLandmarkContest&DatasetsIBUGContest()Datasets:LFPW,AFW,HELEN,IBUG,andXM2VTSDeepLearningFaceAttributesintheWild()Large-scaleCelebFacesAttributes(CelebA)Dataset:Morethan

200K

faceimagesEachwith

40attributes

annotationsProblemsBuildatextsearchengineBuildaimagesearchengineObjectdetection,tracking,recognitionObjectdetection,tracking,recognitionusing3DcameraFacedetection,facerecognitionHumandetection,humantrackingmendsystemImagerecoveryBuildatextsearchengineBasicknowledgeonInformationRetrievalTextsearchengine:CrawlerExtractdatafromthewebpageBuildindexServingtheserviceBasicCrawlerOperationsAwebcrawler,robotorspiderAprogramthatiscapableofiterativelyandautomaticallyBeginwithknown“seed”URLsFetchandparsethemExtractURLstheypointtoPlacetheextractedURLsonaqueueFetcheachURLonthequeueandrepeatStoringthescrapeditem34CrawlerScrapy

AfastandpowerfulscrapingandwebcrawlingframeworkEasilyextensible,portableandpythonDocument:

Opensourcejobqueues

Python-RQ

Redis

ZeroMQ

ExtractDatafromtheHTMLSourceHTMLhasthestructureofaDOM(DocumentObjectModel)treeUnfortunatelyactualHTMLisoftenincorrectExtractdatafromtheHTMLBeautifulSouplxmlSelectingnodesinXMLXPathStoringtheScrapedItemFileJSONformat

Pickleformat

DatabaseSQLMySQLSQLiteKey-ValueRedisMongoDBWordsegmentationandIndexingChinesewordsegmentationJieba

NLPIR

IndexLuceneXapianSphinxmendersystemmenderSystemsaresoftwaretoolsandtechniquesprovidingsuggestionsforitemstobeofusetoauser.Thesuggestionsrelatetovariousdecision-makingprocesses,suchaswhatitemstobuy,whatmusictolistento,orwhatonlinenewstoread.Methods:Content-basedfilteringCollaborativefilteringmendersystemPaperKantorPB,RokachL,RicciF,etal.mendersystemshandbook[M].Springer,2011.KorenY,BellR,VolinskyC.Matrixfactorizationtechniquesformendersystems[J].Computer,2009,42(8):30-37.ContestKDDCupNetflixPrizeConferences&JournalsSIGIR,SIGKDD,IEEETKDEBuildaimagesearchengineBasicknowledgeonImageProcessingExtractfeaturesforimagesBuildindex(invertedindexnolongerworks)ImagesearchengineforZhejiangUniversityObjectdetection,tracking,recognitionBasicknowledgeonComputerVisionDemoModelImageUserImageChangeFaceResultWorkflowFaceDetectionFaceLandmarkSkinAdjustmentAlignmentbyUserFaceLandmarkRelatedworksZhou,Erjin,etal."ExtensiveFacialLandmarkLocalizationwithCoarse-to-FineConvolutionalNetworkCascade."ComputerVisionWorkshops(ICCVW),2013IEEEInternationalConferenceon.IEEE,2013.D.Chen,S.Ren,Y.Wei,X.Cao,J.Sun.JointCascadeFaceDetectionandAlignment.ECCV2014.Robustfacelandmarkestimationunderocclusion.X.P.Burgos-Artizzu,P.PeronaandP.Dollár.ICCV2013.DatasetandBenchmarkAFLW:300-W:COFW:SkinAdjustmentRelatedWorksDigitalImageProcessing:Reinhard,Erik,etal."Colortransferbetweenimages."IEEEComputergraphicsandapplications21.5(2001):34-41.Objectdetection,tracking,recognitionusing3DcameraBasicknowledgeonComputerVisionRGB-DSensorsKinect：Kinectfor360&KinectforWindowsPrimeSense：PrimeSenseSensor&ASUSXtionProLiveIntelRGB&DepthVideoDemoDriverWindowkinectSDK

Freenect

OpenNI

BasicComputerVision：Cameracoordinatetransformation.Imagefeature.Imageprocessing.OpenCV..GPUParallelComputing：CudaOpenCLDeepLearning：CNNReferenceJafari,O.H.;Mitzel,D.;Leibe,B.,"Real-timeRGB-Dbasedpeopledetectionandtrackingformobilerobotsandhead-worncameras,"RoboticsandAutomation(ICRA),2014Berclaz,J.,Fleuret,F.,Turetken,E.,&Fua,P.(2011).Multipleobjecttrackingusingk-shortestpathsoptimization.ImageRecoveryBackground55MatrixCompletionPredictingtheuserbehaviorinthemendersystemRecoveringthemissinginformationofpicturesFoundationofMathematics56MatrixTheory:MatrixAnalysis:RogerA.Horn,CharlesR.JohnsonMatrixComputation:GeneH.Golub,CharlesF.VanLoanFoundationofMathematics57ProbabilityTheoryAProbabilityTheoryofPatternRecognition:LucDevroye,LszlGyoerfi,GaborLugosiProbabilityandStatistics:MorrisH.DeGroot,MarkJ.SchervishFoundationofMathematics58OptimizationTheoryConvexOptimization:LucDevroye,StephenBoyd,

LievenVandenbergheConvexAnalysisandNonlinearOptimization:TheoryandExamples:JonathanM.Borwein,AdrianS.LewisCompressedSensing59Compressedsensingisa

signalprocessing

techniqueforefficientlyacquiringandreconstructinga

signal,byfindingsolutionsto

underdeterminedlinearsystems.NearOptimalSignalRecoveryFromRandomProjections:UniversalEncodingStrategies?AnintroductiontocompressivesamplingTheoryandApplicationsofCompressedSensingRobustuncertaintyprinciples:ExactsignalreconstructionfromhighlypletefrequencyinformationMatrixCompletion60Thekeyideaofmatrixcompletionisthatalowrankandincoherentmatrixwithmissingvaluecanbeperfectlyreconstructedsuingaconvexoptimizationproblemwiththeoreticalguarantee.Thepowerofconvexrelaxation:Near-optimalmatrixcompletion,E.J.Candes,T.Tao,2009MatrixCompletionfroma