Course Introduc;on - IIT Hyderabad › ~vineethnb › teaching › f2016 › ... · CS6510 - Applied Machine Learning 2 • “A breakthrough in machine learning would be worth ten

CS6510AppliedMachineLearning

CourseIntroduc;on

6Aug2016

VineethNBalasubramanian

Afewrecentquotes

5-Aug-16CS6510-AppliedMachineLearning

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•  “AbreakthroughinmachinelearningwouldbeworthtenMicrosoGs”(BillGates,Chairman,MicrosoG)•  “MachinelearningisthenextInternet” (TonyTether,Director,DARPA)• Machinelearningisthehotnewthing” (JohnHennessy,President,Stanford)•  “WebrankingstodayaremostlyamaUerofmachinelearning”(PrabhakarRaghavan,ex-Dir.Research,Yahoo)•  “Machinelearningisgoingtoresultinarealrevolu;on”(GregPapadopoulos,CTO,Sun)•  “Machinelearningistoday’sdiscon;nuity” (JerryYang,ex-CEO,Yahoo)

WhatisMachineLearning?


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WhatisMachineLearning?


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• Makingpredic;onsordecisionsfromdata• “Programmingcomputerstoop;mizeaperformancecriterionusingexampledataorpastexperience”(EthemAlpaydin,MachineLearning,2010)• “AcomputerprogramissaidtolearnfromexperienceEwithrespecttosomeclassoftasksTandperformancemeasureP,ifitsperformanceattasksinT,asmeasuredbyP,improveswithexperienceE.”(TomMitchell,MachineLearning,1997)• “Learninggeneralmodelsfromadataofpar;cularexamples”• “Buildamodelthatisagoodandusefulapproxima1ontothedata.“

Today


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Source:Domingos

Tradi'onalProgramming

MachineLearning

ComputerData

ProgramOutput

ComputerData

OutputProgram

Magic?


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No,morelikegardening• Seeds=Algorithms• Nutrients=Data• Gardener=You• Plants=Programs

Source:Domingos

RelatedTerms


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MachineLearning,DataMining,KnowledgeDiscovery,Ar;ficialIntelligence,Sta;s;calLearning,PaUernRecogni;on,

Computa;onalLearning

WhenisMachineLearningUsed?


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• Humanexper;sedoesnotexist•  E.g.naviga;ngonMars

• Humansareunabletoexplaintheirexper;se•  E.g.speechrecogni;on

•  Solu;onchangesin;me•  E.g.rou;ngonacomputernetwork

•  Solu;onneedstobeadaptedtopar;cularcases•  E.g.userbiometrics

• Dataischeapandabundant;knowledgeisexpensiveandscarce

Applica;onsofMachineLearning


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MoreMLApplica;ons


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•  Science(Astronomy,neuroscience,medicalimaging,•  bio-informa;cs)•  Environment(energy,climate,weather,resources)•  Retail(Intelligentstockcontrol,demographicstore•  placement)•  Manufacturing(Intelligentcontrol,automatedmonitoring,•  detec;onmethods)•  Security(Intelligentsmokealarms,frauddetec;on)•  Marke;ng(promo;ons,...)•  Management(Scheduling,;metabling)•  Finance(creditscoring,riskanalysis...)•  Webdata(informa;onretrieval,informa;onextrac;on,...)

MoreRecentMLApplica;ons


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• AlphaGo!• Automa;ngEmployeeAccessControl• Iden;fyingwhalesinoceanbasedonaudiorecordings• Predictwait;mesforpa;entsinemergencyrooms• Extractheartfailurediagnosiscriteriafromfree-textphysiciannotes• Predic;nghospitalreadmissions• Is(s)heapyschopath?Source:hUp://www.forbes.com/sites/85broads/2014/01/06/six-novel-machine-learning-applica;ons/#6b6f9a9e67bf

WhenareMLalgorithmsnotneeded?


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• Whentherela;onshipsbetweenallsystemvariables(input,output,andhidden)iscompletelyunderstood!

• ThisisNOTthecaseforalmostanyrealsystem!

OverviewofML


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•  Supervisedlearning•  Predictanoutputywhengivenaninputx•  Forcategoricaly:classifica;on.•  Forreal-valuedy:regression.

• Unsupervisedlearning•  Createaninternalrepresenta;onoftheinput,e.g.clustering,dimensionality•  ThisisimportantinmachinelearningasgennglabelsisoGendifficultandexpensive

• OtherareasofML•  Learningtopredictstructuredobjects(e.g.,graphs,trees)•  Reinforcementlearning(learningfrom“rewards”)•  Semi-supervisedlearning(combinessupervised+unsupervised)

Classifica;on(SupervisedLearning)


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Classifica;on(SupervisedLearning)


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Regression(SupervisedLearning)


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Ranking(SupervisedLearning)


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Givenaqueryandasetofwebpages,rankthemaccordingtorelevance

• Otherapplica;ons•  Userpreference,e.g.Nerlix“MyList”--moviequeueranking•  Flightsearch(searchingeneral)•  …

Clustering(UnsupervisedLearning)


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ReinforcementLearning


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•  Learningapolicy:Asequenceofoutputs• Nosupervisedoutputbutdelayedreward•  E.g.Gameplaying•  E.g.Robotinamaze

• Mul;pleagents,par;alobservability,...•  Examples:•  hUps://www.youtube.com/watch?v=DCjbk4m1G6I•  hUps://www.youtube.com/watch?v=VCdxqn0fcnE

DimensionalityReduc;on


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•  Largesamplesizeisrequiredforhigh-dimensionaldata• Queryaccuracyandefficiencydegraderapidlyasthedimensionincreases•  Strategies•  Featurereduc;on•  Featureselec;on•  Manifoldlearning•  Kernellearning

MLProblems


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MLinPrac;ce


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• Understandingdomain,priorknowledge,andgoals• Dataintegra;on,selec;on,cleaning,pre-processing,etc.• Learningmodels• Interpre;ngresults• Consolida;nganddeployingdiscoveredknowledge• Loop

TrainingandTes;ngMLModels


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Trainingset(observed)

Universalset(unobserved)

Tes;ngset(unobserved)

Dataacquisi;on Prac;calusage

TrainingandTes;ngMLModels


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•  Trainingistheprocessofmakingthesystemabletolearn.• Nofreelunchrule:•  Trainingsetandtes;ngsetcomefromthesamedistribu;on•  Needtomakesomeassump;onsorbias

TypesofModels


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• Induc;vevsTransduc;veLearning• OnlinevsOfflineLearning• Genera;vevsDiscrimina;veModels• ParametricvsNon-ParametricModels

MLDatasets


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• UCIRepository:hUp://www.ics.uci.edu/~mlearn/MLRepository.html• Statlib:hUp://lib.stat.cmu.edu/

MLResources


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• MOOCs•  Coursera

• Conferences/Journals•  JMLR,MachineLearning,IEEETransac;onsonNeuralNetworksandLearningSystems,IEEETransac;onsonPaUernAnalysisandMachineIntelligence,AnnalsofSta;s;cs•  ICML,NIPS,ACMSIG-KDD,IJCAI,AAAI,ICDM

Mathema;calBasis


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• Func;ons,LogarithmsandExponen;als• Vectors,DotProducts,Orthogonality• Matrices,MatrixOpera;ons,LinearTransforma;ons,Eigendecomposi;on• Calculus,Differen;a;on,Integra;on• ProbabilityandSta;s;cs• Func;onalAnalysis,HilbertSpaces

CourseDetails


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•  Timings/Loca'on:•  Sat9:30am–12:30pm•  Un;lmid-OctoberforEMDSstudents•  Block-ALH1(LH2occasionally,ifrequired)

•  Instructor:VineethNB•  Email:[email protected]•  Office:Block-E,324

•  TAs•  ArghyaPal,SupriyaPandhre,MausamJain,AkileshB,SahilManocha,HrishikeshVaidya

• Web:•  hUp://www.iith.ac.in/~vineethnb/teaching/f2016/cs6510-aml.htm•  WewillsoonhaveaGoogleClassroomportalforclassmanagement

CourseTopics


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•  Introduc;ontoMachineLearning:OverviewandApplica;ons•  Classifica;onMethods• MachineLearningSystemDesign•  RegressionMethods•  ClusteringMethods• DimensionalityReduc;onMethods•  FeatureSelec;onMethods•  AnomalyDetec;onMethods•  AdvancedMethodsMethods

•  GraphicalModels,DeepLearning,NewSenngs(Ac;veLearning,TransferLearning,StructuredPredic;on,Mul;taskLearning,Mul;pleInstanceLearning

Topicsnottobecovered(likely)


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• DeepLearning(notinits“depth”,atleast)• BayesianNetworks(notindepth)• ReinforcementLearning• LearningTheory

CourseEvalua;onRubric


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• 15%:Quizzes• 25%:Homework(Theory+Programming)• 30%:Compe;;veCodingAssignments(Challengestyle)• 30%:End-semesterexamina;on

•  5gracedaystostartwith–pleaseusethemwisely.Maynotapplytosomedeadlines,whichwillbepointedout.

•  Best80%(approx,tenta;ve)ofquizzeswillbeconsidered•  NotaUemp;ngtheend-semesterexamwillresultinanFR.

Programming


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• Python• Libraries•  Numpy,Scipy–numerical/scien;ficcompu;ng,linearalgebra•  Matplotlib–forplonng•  Scikitlearn–formachinelearning

References


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• KeyReferences•  PaUernRecogni;onandMachineLearning,byChristopherBishop•  Ar;cles/Blogs/Papers/MOOCsonline

• OtherRecommendedReferences•  R.Duda,P.Hart&D.Stork,PaUernClassifica;on(2nded.)•  T.Mitchell,MachineLearning,

Appropriatereadingmaterialsforlectureswillbeposted

Homework


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• Gothroughremedialvideosformathfounda;onsat:•  hUps://www.youtube.com/channel/UC7gOYDYEgXG1yIH_rc2LgOw/playlists

• Programming•  LearnPython

•  hUps://try.jupyter.org/•  hUps://docs.python.org/3/tutorial/•  VideoTutorials:hUps://www.youtube.com/watch?v=cpPG0bKHYKc

•  Noteofcau;on:Python2.7vsPython3.4•  hUp://sebas;anraschka.com/Ar;cles/2014_python_2_3_key_diff.html

•  PlaywithNumpy

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Course Introduc;on - IIT Hyderabad › ~vineethnb › teaching › f2016 › ... · CS6510 - Applied Machine Learning 2 • “A breakthrough in machine learning would be worth ten