CSE517A– MACHINELEARNING
Spring2019MarionNeumann
COURSEOVERVIEW&STRUCTURE
ABOUT
• MarionNeumann• office:JolleyHall222• officehours:TUE 11:30-12:30pm• contact:usePiazza(http://piazza.com/wustl/spring2019/cse517a)
• Lectures:TUE&THU• 10-11:30pmin Louderman458
• Coursewebsite: https://sites.wustl.edu/neumann/courses/cse517a/sp19/
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Bookmarkme!!!
Youareareal person!
READING
CourseBook• AFirstCourseinMachineLearning,
RogersandGirolami,2nded.(Wewillusethisbookforreadings,mathematicalderivations, andhomework problems.)
Allreadingwillbepostedonthecoursewebpageandisconsideredcoursematerialsand exam-relevant!
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Resources:https://sites.wustl.edu/neumann/courses/cse517a/resources/
Getacopyofthisbook!
GRADINGANDPOLICIES
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CourseSyllabus:https://sites.wustl.edu/neumann/courses/cse517a/syllabus/
Bystayingenrolledinthiscourseyouconfirmthatyouread,understood and agreed
thecoursesyllabus.
COLLABORATIONPOLICY
Collaboration:yes• discusscoursematerialswithotherstudentsà joinastudygroup
Cheating:no• donotcopyanswers/codeorpartsofanswers/codefromanyoneelseorfromanymaterialyoufindonline
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AutogradercomparesyoursolutionstothesolutionsofotherstudentsandsolutionsavailableONLINE!
TAswillactivelywatchoutforsimilarsolutionstowrittenhws!
ACADEMICINTEGRITY
• Everythingthatyouturninforthiscoursemustbeyourownwork.Ifyouwillfullymisrepresentsomeoneelse’sworkasyourown,youareguiltyofcheating.
• Providing yourcoursework(writtenorcode)in anyform toothers(e.g.hostingcodeonapublicGitHubrepository)isaviolationoftheacademicintegritypolicy.
• zerotolerance à alloccurrenceswillbereported• Finthecourse• referredtotheSchoolofEngineeringDisciplineCommittee• thiscanleadtoexpulsionfromtheUniversity,aswellaspossibledeportationforinternationalstudents.
• Ifyoucopyfromanyoneintheclassbothpartieswillbepenalized,regardlessofwhichdirectiontheinformationflowed.
6Thisisyouronlywarning.
COURSEOBJECTIVE• derive,• understand,• implement,• analyze,and• apply(advanced)machinelearningmethods
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THISCOURSE
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MLtechniques
• structuralriskminimization
• MLEvsMAP• unsupervisedlearning• dimensionality
reduction• semi-supervised
learning• graph-basedML
MLmodels
• kernelmethods• GPs• neuralnetworks• naïveBayes• GMMs• PCA/SVD
...seeRoadmap onthecoursewebpage
PracticalML
• multi-classclassification• featureselection• dimensionalityreduction
Contentsmaybesubjecttochanges!
BACKGROUND&PREREQUISITES
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• TheoreticalfoundationsofML• CSE417t• keepthecse417tcoursebookLearningfromDataàwewillusesomeoftheeChapters notcoveredincse417t
• Programmingà Python>=3.4
• ThreepillarsofML- probabilitiesandstatistics- matricesandlinearalgebra- multivariatecalculusandoptimization
CSE517a
ABOUTTHISCOURSE• Take thiscourseif...
• youalreadyknowaboutthetheoreticalfoundationsofML• youarealreadyveryfamiliarwithsimpleMLmethodssuchaslinear
models,perceptron,decisiontrees,andnearest-neighbormethods• youwanttounderstand advancedMachineLearningmethodsand
techniques• youarecomfortablewithadecentamountofmathematics• youarenotscaredofprogramming(alot!)
• Don’ttakethiscourseif...• youonly wanttoapplyMachineLearningmethods(usingWEKAorscikit-
learntoolboxes)• matrices scareyou• youdon’trememberhowtotakederivatives• youwantaneasyA• youhavenot takenCSE417t
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PREVIOUSSTUDENTS’COMMENTS• “[...]Requiresagoodknowledgeinmathandderivatives.”
• “ATONofwork,butmostlyworthitforaveryvaluableskill.”
• “greatcourse,butpreparetoworkyourbuttoff.”
• “Thetopicswereprettycomplicatedanddifficulttounderstandquickly.Iwouldhavepreferredaslightlyslowerpace.”
• “It'smostlyamathclass”
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WHATISMACHINELEARNING?Machine Learning
Data
ProgramOutput
Computer
Traditional CS:
Machine Learning:
Data
OutputProgram
Computer
Machine Learning
Data
ProgramOutput
Computer
Traditional CS:
Machine Learning:
Data
OutputProgram
Computer12
DataOutput
Computer
Program
Computer
Data
Output
Machine Learning
Training: Testing:
Machine Learning:
Contentsinthisslidemaybesubjecttocopyright.AdoptedfromKillianWeinberger.Thanks,Killian!.
DEFINITION
Mitchell1997:AcomputerprogramAissaidtolearn fromexperienceEwithrespecttosomeclassoftasksTandperformancemeasureP,ifitsperformanceattasksinT,asmeasuredbyP,improves withexperienceE.
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