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1、CS229T/STAT231:StatisticalLearningTheory(Winter2014)PercyLiangLastupdatedWedFeb12201409:24Theselecturenoteswillbeupdatedperiodicallyasthecoursegoeson.Pleaseletusknowwhenyoundtypos.SectionA.1describesthebasicnotationanddenitions;SectionA.2describessomebasictheorems.LecturesThenotesareorganiz
2、edaccordingtotopic.However,thelecturesthemselveswilloftenspillacrosstopics,sothematerialactuallycoveredineachlectureisexplicitlydelimitedandlinkedbelow.Lecture1/6:Overview,onlinelearningLecture1/8:OnlinelearningLecture1/13:OnlinelearningLecture1/15:OnlinelearningLecture1/20:Noclass(Marti
3、nLutherKingDay)Lecture1/22:OnlinelearningLecture1/27:KernelmethodsLecture1/29:KernelmethodsLecture2/3:KernelmethodsLecture2/5:UniformconvergenceLecture2/10:UniformconvergenceLecture2/12:UniformconvergenceLecture2/17:Noclass(PresidentsDay)Lecture2/19:UniformconvergenceLecture2/24:Dir
4、ectanalysisLecture2/26:DirectanalysisLecture3/3:Spectralmethods1Lecture3/5:SpectralmethodsLecture3/10:SpectralmethodsLecture3/12:Finalprojectpresentations21Overview[beginlecture1/6](1/6)1.1Whatisthiscourseabout?Machinelearninghasbecomeanindispensiblepartofmanyapplicationareas,rangingfro
5、mscience(biology,neuroscience,psychology,astronomy,etc.)toengineering(nat-urallanguageprocessing,computervision,robotics,etc.).Butmachinelearningisnotonecoherentapproach,butratherconsistsofadazzlingarrayofseeminglydisparateframeworksandparadigmsspanningclassication,regression,clustering,matr
6、ixfac-torization,graphicalmodels,probabilisticprogramming,etc.Thiscoursetriestouncoverthecommonstatisticalprinciplesunderlyingthisdiversearrayoftechniques,usingtheoreticalanalysisasthemaintoolforunderstanding.Wewilltrytoanswertwoquestions:{Whendolearningalgorithmswork(orfail)?Anyonewithhands
7、-onmachinelearningexperiencewillprobablyknowthatlearningalgorithmsrarelyworkthersttime.Whentheydon't,adeepertheoreticalunderstandingcanoeranewperspectiveandcanaidintroubleshooting.{Howdowemakelearningalgorithmsbetter?Theoreticalanalysesofte