Stats5 Seminar: Machine Learningsmyth/courses/stats5/onlineslides/...Feb 20 John Brock Cylance, Inc Data Science and CyberSecurity Feb 27 Video Lecture (Kate Crawford) Microsoft Research

Stats5Seminar:MachineLearning

Winter2018

ProfessorPadhraicSmythDepartmentsofComputerScienceandStatisticsUniversityofCalifornia, Irvine

P.Smyth:Stats5:DataScience Seminar,Winter2018:2

ClassOrganization

• Meetweeklyfor40minuteseminarwith5-10minutediscussion

• 8topics(withguestspeakers),weeks2through9– Youareencouraged toaskquestionsduringandafterthetalks

• Introandwrap-uptalksinweeks1and10

• ClassWebsiteisatwww.ics.uci.edu/~smyth/courses/stats5– Slidesandrelatedmaterialswillbeposted duringthequarter

Date Speaker DepartmentOr Organization Topic

Jan9 PadhraicSmyth ComputerScience IntroductiontoDataScience

Jan16 Padhraic Smyth ComputerScience Classification AlgorithmsinMachineLearning

Jan23 MichaelCarey ComputerScience DatabasesandDataManagement

Jan30 SameerSingh ComputerScience StatisticalNaturalLanguageProcessing

Feb6 Zhaoxia Yu Statistics AnIntroductiontoClusterAnalysis

Feb13 ErikSudderth ComputerScience ComputerVision andMachineLearning

Feb20 JohnBrock Cylance, Inc DataScienceandCyberSecurity

Feb27 VideoLecture(KateCrawford)

MicrosoftResearchandNYU BiasinMachineLearning

Mar6 MattHarding Economics DataScienceinEconomics andFinance

Mar13 PadhraicSmyth ComputerScience Review:PastandFutureofDataScience

ScheduleofLectures

SubmissionofReviewForms(Weeks2to10)

• SubmitReviewformsforLectures2through10• Availableathttp://www.ics.uci.edu/~smyth/courses/stats5/Forms/

• Reviewformswillbeavailableonlineatthestartofeachclass– Afewrelativelyshortquestionsbasedonthelecturethatday– Needstobesubmitted toEEEby12:15foreachlecture– Bringyour laptoporotherdevice

• Requirementstopasstheclass– Attendandsubmit reviewform for least8lecturesforweeks2through 10

(allowedtomissoneifyouneedtoforsomereason)

• Nofinalexam:pass/failbasedonattendanceandreviewforms

OutlineofToday’sTopic

• Whatismachinelearning?

• Classificationalgorithms

• Examplesfromimageandsequenceclassification

• Conclusionsanddiscussion

[Acknowledgement toProfessor AlexIhler forvariousslidesandfigures inthislecture]

WhatisMachineLearning?

Machinelearning(ML)

• Learningmodelsfromdata• Makingpredictions(ordecisions)• Gettingbetterwithexperience(data)• Problemswhosesolutionsare“hardtodescribe”

Typesofmachinelearningproblems

• Supervisedlearning– “Labeled”trainingdata– Everyexamplehasadesiredtargetvalue(a“knownanswer”)– Rewardpredictionsclosetotarget;penalizepredictionswithlargeerrors

– Classification:adiscrete-valuedprediction– Regression:acontinuous-valued prediction

• Supervisedlearning– “Labeled”trainingdata– Everyexamplehasadesiredtargetvalue(a“bestanswer”)– Rewardpredictionbeingclosetotarget

– Classification:adiscrete-valuedprediction– Regression:acontinuous-valued prediction

– Recommendersystems12

987654321

455? 311

3124452

534321423

245424

5224345

423316

movies

• Supervisedlearning– Trainingdatahaslabelsortargetvalues

• Unsupervisedlearning– Trainingdatahasnolabelsortargetvalues– Interestedindiscoveringnaturalstructureindata– Oftenusedinexplorationofdata,e.g.,inscience,inbusiness– Example:

• Clusteringcustomersormedicalpatientsintogroups• Discoveringanumericalrepresentationofwordsormovies

Datain2Dimensionswith5Clusters

SeeLecturebyProfZhaoxia YulaterthisquarteronClusteringAlgorithms

Embeddings ofWordsasVectors

From:https://www.mathworks.com/help/examples/textanalytics/

Figure from Koren, Bell, Volinksy, IEEE Computer, 2009

• Supervisedlearning• Unsupervisedlearning

• Reinforcementlearning– Algorithmgetsindirectfeedbackonitsprogress(ratherthancorrect/incorrect)– E.g.,aprogramlearningtoplaychess,orGo,oravideogame– E.g.,anautonomous vehiclelearninghowtonavigateacity– Mathematicalmodelsfordelayedreward,creditassignment,explore/exploit

ClassificationusingSupervisedLearning

LearningaClassificationModel

PatientID Zipcode Age …. Test Score Diagnosis

18261 92697 55 83 1

42356 92697 19 99 1

00219 90001 35 21 0

83726 24351 0 35 0

TrainingData

Learningalgorithmlearnsafunctionthattakesvaluesonthelefttopredictthevalue(diagnosis)ontheright

MakingPredictionswithaClassificationModel

PatientID Zipcode Age …. Test Score Diagnosis

18261 92697 55 83 1

42356 92697 19 99 1

00219 90001 35 21 0

83726 24351 0 35 0

12837 92697 40 70 ??

72623 92697 32 44 ??

Wecanthenusethemodeltomakepredictionswhentargetvaluesareunknown

TrainingData

TestData

0 10 20 30 40 50 60 70 80 900

EEachdotisa2-dimensionalpointrepresentingoneperson=[AGE,MONTHLYINCOME]

0 10 20 30 40 50 60 70 80 900

Goodboundary?

Betterboundary?

Bluedots=goodloansReddots=badloans

0 10 20 30 40 50 60 70 80 900

Amuchmorecomplexboundary– butperhapsoverfitting tonoise?

BasicConcepts

• Thecurverepresentsaclassifier(amodel,apredictor)– Pointsononesideofthelinegetclassifiedasoneclass– Pointsontheother sidegetclassifiedastheotherclass– Onceweknowthecurvewecantakenewpointsandclassifythem

• Thecurveisrepresentedinternallybyasetofcoefficients– Thesearealsoknownas“parameters”or“weights”

• Thealgorithmsystematicallyadjuststhecoefficientsontrainingdatatoreducetheerrorasmuchasitcan

• Thisprocessoffindingtheweightsisknownas“learningamodel”

• Foundationalideasarefromstatisticsandoptimization

0 10 20 30 40 50 60 70 80 900

Initialguessforcoefficients(notverygood,higherror)

0 10 20 30 40 50 60 70 80 900

Initialguessforcoefficients(notverygood,higherror)

Finalsolutionforcoefficients(muchbetter,lowerror)

010 20

30 4050

60 7080

14000-3

AGEMONTHLY INCOME

Noweachdotisa3-dimensionalpointrepresentingoneperson=[AGE,MONTHLYINCOME,ASSETS]

Ourboundarylinewillnowbecomeaplane

HowDoesthisWorkinPractice?

• Weusecomputeralgorithmstosearchforthebestlineorcurve

• Thesesearchalgorithmsarequitesimple1. Startwithaninitialrandomguessforcoefficients2. Changethecoefficientsslightly toreducetheerror

(canusecalculustodothis)3. Movetothenewcoefficients4. Keeprepeatinguntil“convergence”

• Thissearchcanbedone10,100,1000,or1million“dimensions”….with10’sofmillionsofexamples

• Thissearchprocessisatthecoreofmachinelearningalgorithms

KeyPoints

• Werepresentourtrainingdataaspointsinamulti-dimensionalspace– Howdoweobtainthelabelsforthedatapoints?

• Wewanttofindaboundarycurvethatcanseparatepointsintotwoclasses

• Thecurvesarerepresentedbysetsofcoefficients(orweights)

• Machinelearningalgorithmsusesearch(oroptimization)toautomaticallyfindthecoefficientswiththelowesterroronthetrainingdata

IftheModelistooComplexitcanOverfit

Toosimple?

Toocomplex? Aboutright?

NeuralNetworkClassifiers

Machine Learning Notation

Features x e.g.,pixelinputs(usuallyamultidimensionalvector)

Targets y e.g.,truelabelforanimage:“cat”or“nocat”

Predictionsŷ e.g.,model’spredictiongiveninputs,e.g.,“cat”

Error e(y,ŷ ) e.g.,e=0ifpredictionmatchestarget,1otherwise

Parametersθ e.g.,weights,coefficientsspecifyingthemodel

Example:ASimpleLinearModel

Themachinelearningalgorithmwilllearnaweightforeacharrowinthediagram

Thisasimplemodel:oneweightperinput

ASimpleNeuralNetwork

Herethemodellearns3differentfunctionsandthencombinestheoutputsofthe3tomakeaprediction

Thisismorecomplexandhasmoreparametersthanthesimplemodel

HiddenLayer

Output

Inputs

HiddenLayer

Output

Inputs

HiddenLayer

Output

Inputs

DeepLearning:ModelswithMoreHiddenLayers

Wecanbuildonthisideatocreate“deepmodels”withmanyhiddenlayers

Veryflexibleandcomplexfunctions

HiddenLayer 1

HiddenLayer 2

Output

Inputs

Figure from http://parse.ele.tue.nl/

ExampleofaNetworkforImageRecognition

Mathematicallythisisjustafunction(acomplicatedone)

ABriefHistoryofNeuralNetworks…

• ThePerceptronEra:1950sand60s– Greatoptimism withperceptrons(linearmodels)....– ...untilMinsky,1969:perceptronshadlimitedrepresentationpower– Hardproblemsrequirehiddenlayers....buttherewasnotrainingalgorithm

• TheBackpropagationEra:Late1980stomid-90’s– Invention ofbackpropagation– trainingofmodelswithhiddenlayers– Wildenthusiasm(intheUSatleast)....NIPSconference,funding,etc– Mid1990’s:enthusiasmdiesout: trainingdeepNNsishard

• TheDeepLearningEra:2010-present– 3rdwaveofneuralnetworkenthusiasm– Whathappenedsincemid90’s?

• Muchlargerdatasets• Muchgreatercomputationalpower• Fastoptimizationtechniques

LearningviaGradientDescent

Finding good parameters

• Wanttofindparametersθ whichminimizeourerror…

• Thinkofacost“surface”:errorresidualforthat θ…

Gradientdescent

• Howtochangeθ toimproveJ(θ)?

• ChooseadirectioninwhichJ(θ)isdecreasing

Gradientdescent

• Howtochangeθ toimproveJ(θ)?

• ChooseadirectioninwhichJ(θ)isdecreasing

• Derivative

• Positive=>increasing• Negative=>decreasing

Gradientdescentinmoredimensions

• Gradientvector

• Indicatesdirectionofsteepestascent(negative=steepestdescent)

Commentsongradientdescent

• Simpleandgeneralalgorithm– Usableinbroadvarietyofmodels

• Localminima– Sensitivetostartingpoint

ImageClassificationExamples

Example:ClassifyingHandwrittenDigits

Whatthedatalooksliketothehumaneye

Inputs:pixelvaluesfromeachimageOutput:10possibleclasses(0,1,…,9)

PixelInputsRepresentedNumerically

From https://www.tensorflow.org/get_started/mnist/beginners

Example:ClassifyingHandwrittenDigits

ClassificationAccuracyhasgonefrom93%to99.9%inthepast10years

ExamplesofErrorsmadebytheNeuralNetworkClassifier

Image from http://neuralnetworksanddeeplearning.com/chap6.html

Human label (“truth”)

Label predicted by the classifier

Russakovsky etal,ImageNet LargeScaleVisualRecognitionChallenge, 2015

Trainingdatainputsx=rawpixelvalueslabelsy=valuesfrom1to1000

Trainedonmillionsofimages

Howisnetworkstructuredetermined?Essentiallytrial-and-error(expensive!)

DeepNetworkarchitectureforGoogLeNet network,27layers

Figure from Kevin Murphy, Google, 2016

Figure from Krizhevsky, Sutskever, Hinton, 2012

Figure from Lee et al., ICML 2009

SequencePredictionExamples

LearningbyPredictingwhat’sNext

• Examples– Predictthenextwordapersonwilltypeorspeak,givenwordsuptothispoint– Predictthevalueofthe DowJonestomorrowafternoon,givenhistory

• Wecanusethesamegeneralmethodologiesasbefore– Modelnowusespastdatatopredictnextevent

• Applications– Speechrecognition– Auto-suggest inhumantyping– Machinetranslation– Consumermodeling– Chatbots– …andmore

Example:PredictingtheNextCharacter

Figure from http://cs.stanford.edu/people/karpathy/recurrentjs/

Example:PredictingCharacterswithaRecurrentNetwork

Figure from http://cs.stanford.edu/people/karpathy/recurrentjs/

OutputfromaModelLearnedonShakespeare

KING LEAR: O, if you were a feeble sight, the courtesy of your law, Your sight and several breath, will wear the gods With his heads, and my hands are wonder'd at the deeds, So drop upon your lordship's head, and your opinion Shall be against your honour.

Second Senator: They are away this miseries, produced upon my soul, Breaking and strongly should be buried, when I perish The earth and thoughts of many states.

DUKE VINCENTIO: Well, your wit is in the care of side and that.

Examples from “The Unreasonable Effectiveness of Recurrent Neural Networks”, Andrej Kaparthy, blog, http://karpathy.github.io/2015/05/21/rnn-effectiveness/

OutputfromaModelLearnedonCookingRecipes

From https://gist.github.com/nylki/1efbaa36635956d35bcc

OutputfromaModelLearnedonSourceCode

OutputfromaModelLearnedonMathematicsPapers

OutputfromaModelLearnedfromUSPresidentSpeeches

From https://medium.com/@samim/

LimitationsofClassificationAlgorithms

ADeepNeuralNetworkforImageRecognitionFromNguyen,Yosinski, Clune, CVPR2015

ADeepNeuralNetworkforImageRecognition

ImagesusedforTraining NewImages

FromNguyen,Yosinski, Clune, CVPR2015

ADeepNeuralNetworkforImageRecognitionFromNguyen,Yosinski, Clune, CVPR2015

FromNguyen,Yosinski, Clune, CVPR2015

ADeepNeuralNetworkforImageRecognition

Date Speaker DepartmentOr Organization Topic

Jan9 PadhraicSmyth ComputerScience IntroductiontoDataScience

Jan16 Padhraic Smyth ComputerScience MachineLearning

Jan23 MichaelCarey ComputerScience DatabasesandDataManagement

Jan30 SameerSingh ComputerScience StatisticalNaturalLanguageProcessing

Feb6 Zhaoxia Yu Statistics AnIntroductiontoClusterAnalysis

Feb13 ErikSudderth ComputerScience ComputerVision andMachineLearning

Feb20 JohnBrock Cylance, Inc DataScienceandCyberSecurity

Feb27 VideoLecture(KateCrawford)

MicrosoftResearchandNYU BiasinMachineLearning

Mar6 MattHarding Economics DataScienceinEconomics andFinance

Mar13 PadhraicSmyth ComputerScience Review:PastandFutureofDataScience

ScheduleofLectures

Stats5 Seminar: Machine Learningsmyth/courses/stats5/onlineslides/...Feb 20 John Brock Cylance, Inc Data Science and CyberSecurity Feb 27 Video Lecture (Kate Crawford) Microsoft Research

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