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CSC 4510 – Machine Learning Dr. Mary‐Angela Papalaskari Department of CompuBng Sciences Villanova University Course website: www.csc.villanova.edu/~map/4510/ Lecture 2: History and Overview of Machine Learning 1 CSC 4510 ‐ M.A. Papalaskari ‐ Villanova University

CSC 4510 – Machine Learningmap/4510/02MLhistory-s-u.pdf · 2012-01-26 · Machine learning • Supervised Learning – Classificaon – Regression • Unsupervised learning Others:

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Page 1: CSC 4510 – Machine Learningmap/4510/02MLhistory-s-u.pdf · 2012-01-26 · Machine learning • Supervised Learning – Classificaon – Regression • Unsupervised learning Others:

CSC4510–MachineLearningDr.Mary‐AngelaPapalaskari

DepartmentofCompuBngSciencesVillanovaUniversity

Coursewebsite:

www.csc.villanova.edu/~map/4510/

Lecture2:HistoryandOverviewofMachineLearning

1CSC4510‐M.A.Papalaskari‐VillanovaUniversity

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CSC4510‐M.A.Papalaskari‐VillanovaUniversity 2

“Itwon’ttrulybeanautonomousvehicleunBlyouinstructittodrivetoworkanditheadstothebeachinstead.”

‐ BradTempleton,SoTwaredesignerandaconsultantfortheGoogleprojectonAutonomousVehicles

‐ NYTimes1/24/12‐ hYp://www.nyBmes.com/2012/01/24/technology/googles‐autonomous‐vehicles‐draw‐skepBcism‐at‐legal‐symposium.html?_r=2&nl=technology&emc=techupdateema22

Page 3: CSC 4510 – Machine Learningmap/4510/02MLhistory-s-u.pdf · 2012-01-26 · Machine learning • Supervised Learning – Classificaon – Regression • Unsupervised learning Others:

WhatarethegoalsofAIresearch?

ArBfactsthatACTlikeHUMANS

ArBfactsthatTHINKlikeHUMANS

ArBfactsthatTHINKRATIONALLY

ArBfactsthatACTRATIONALLY

CSC4510‐M.A.Papalaskari‐VillanovaUniversity 3

Page 4: CSC 4510 – Machine Learningmap/4510/02MLhistory-s-u.pdf · 2012-01-26 · Machine learning • Supervised Learning – Classificaon – Regression • Unsupervised learning Others:

ABitofHistory

•  ArthurSamuel(1959)wroteaprogramthatlearnttoplaycheckerswellenoughtobeathim.

CSC4510‐M.A.Papalaskari‐VillanovaUniversity 4

Page 5: CSC 4510 – Machine Learningmap/4510/02MLhistory-s-u.pdf · 2012-01-26 · Machine learning • Supervised Learning – Classificaon – Regression • Unsupervised learning Others:

1940sAdvancesinmathemaBcallogic,informaBontheory,conceptofneuralcomputaBon

 1943:McCulloch&PiYsNeuron  1948:Shannon:InformaBonTheory  1949:HebbianLearning  cellsthatfiretogether,wiretogether

1950sEarlycomputers.Dartmouthconferencecoinsthephrase“arBficialintelligence”andLispisproposedastheAIprogramminglanguage

 1950:TuringTest  1956:DartmouthConference  1958:Friedberg:LearnAssemblyCode  1959:Samuel:LearningCheckers

CSC4510‐M.A.Papalaskari‐VillanovaUniversity 5

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1960sA.I.fundingincreased(mainlymilitary).Famousquote:“WithinageneraBon...theproblemofcreaBng'arBficialintelligence'willsubstanBallybesolved.”Earlysymbolicreasoningapproaches.

 LogicTheorist,GPS,Perceptrons  1969: Minsky & Papert “Perceptrons”

1970sA.I.“winter”–Fundingdriesupaspeoplerealizethisisahardproblem!LimitedcompuBngpoweranddead‐endframeworksleadtofailures.

 eg:MachineTranslaBonFailure

CSC4510‐M.A.Papalaskari‐VillanovaUniversity 6

Page 7: CSC 4510 – Machine Learningmap/4510/02MLhistory-s-u.pdf · 2012-01-26 · Machine learning • Supervised Learning – Classificaon – Regression • Unsupervised learning Others:

1980sRulebased“expertsystems”usedinmedical/legalprofessions.Bio‐inspiredalgorithms(Neuralnetworks,GeneBcAlgorithms).Again:A.I.promisestheworld–lotsofcommercialinvestment

ExpertSystems(Mycin,Dendral,EMYCINKnowledgeRepresentaBonandreasoning:

Frames,Eurisko,Cyc,NMR,fuzzylogicSpeechRecogniBon(HEARSAY,HARPY,HWIM)

ML:  1982: Hopfield Nets, Decision Trees, GA & GP.  1986: Backpropagation, Explanation-Based Learning

CSC4510‐M.A.Papalaskari‐VillanovaUniversity 7

Page 8: CSC 4510 – Machine Learningmap/4510/02MLhistory-s-u.pdf · 2012-01-26 · Machine learning • Supervised Learning – Classificaon – Regression • Unsupervised learning Others:

1990sSomeconcretesuccessesbegintoemerge.AIdivergesintoseparatefields:ComputerVision,AutomatedReasoning,Planningsystems,NaturalLanguageprocessing,MachineLearning…

…MachineLearningbeginstooverlapwithsta4s4cs/probabilitytheory.

 1992: Koza & Genetic Programming  1995: Vapnik: Support Vector Machines

CSC4510‐M.A.Papalaskari‐VillanovaUniversity 8

Page 9: CSC 4510 – Machine Learningmap/4510/02MLhistory-s-u.pdf · 2012-01-26 · Machine learning • Supervised Learning – Classificaon – Regression • Unsupervised learning Others:

2000s

Firstcommercial‐strengthapplicaBons:Google,Amazon,computergames,route‐finding,creditcardfrauddetecBon,spamfilters,etc…

Toolsadoptedasstandardbyotherfieldse.g.biology

CSC4510‐M.A.Papalaskari‐VillanovaUniversity 9

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CSC4510‐M.A.Papalaskari‐VillanovaUniversity 10

2010s…. ??????

Page 11: CSC 4510 – Machine Learningmap/4510/02MLhistory-s-u.pdf · 2012-01-26 · Machine learning • Supervised Learning – Classificaon – Regression • Unsupervised learning Others:

•  Usingmachinelearningtodetectspamemails.

To: [email protected] GET YOUR DIPLOMA TODAY! If you are looking for a fast and cheap way to get a diploma, this is the best way out for you. Choose the desired field and degree and call us right now: For US: 1.845.709.8044 Outside US: +1.845.709.8044 "Just leave your NAME & PHONE NO. (with CountryCode)" in the voicemail. Our staff will get back to you in next few days!

ALGORITHM Naïve Bayes Rule mining

CSC4510‐M.A.Papalaskari‐VillanovaUniversity 11

Page 12: CSC 4510 – Machine Learningmap/4510/02MLhistory-s-u.pdf · 2012-01-26 · Machine learning • Supervised Learning – Classificaon – Regression • Unsupervised learning Others:

•  Usingmachinelearningtorecommendbooks.

ALGORITHMS Collaborative Filtering Nearest Neighbour Clustering CSC4510‐M.A.Papalaskari‐VillanovaUniversity 12

Page 13: CSC 4510 – Machine Learningmap/4510/02MLhistory-s-u.pdf · 2012-01-26 · Machine learning • Supervised Learning – Classificaon – Regression • Unsupervised learning Others:

•  UsingmachinelearningtoidenBfyfacesandexpressions.

ALGORITHMS Decision Trees

Adaboost

CSC4510‐M.A.Papalaskari‐VillanovaUniversity 13

Page 14: CSC 4510 – Machine Learningmap/4510/02MLhistory-s-u.pdf · 2012-01-26 · Machine learning • Supervised Learning – Classificaon – Regression • Unsupervised learning Others:

ALGORITHMS Feature Extraction Probabilistic Classifiers Support Vector Machines + many more….

•  Using machine learning to identify vocal patterns

CSC4510‐M.A.Papalaskari‐VillanovaUniversity 14

Page 15: CSC 4510 – Machine Learningmap/4510/02MLhistory-s-u.pdf · 2012-01-26 · Machine learning • Supervised Learning – Classificaon – Regression • Unsupervised learning Others:

•  MLforworkingwithsocialnetworkdata:detecBngfraud,predicBngclick‐thrupaYerns,targetedadverBsing,etcetcetc.

ALGORITHMS Support Vector Machines Collaborative filtering Rule mining algorithms Many many more….

CSC4510‐M.A.Papalaskari‐VillanovaUniversity 15

Page 16: CSC 4510 – Machine Learningmap/4510/02MLhistory-s-u.pdf · 2012-01-26 · Machine learning • Supervised Learning – Classificaon – Regression • Unsupervised learning Others:

•  ArthurSamuel(1959).MachineLearning:Fieldofstudythatgivescomputerstheabilitytolearnwithoutbeingexplicitlyprogrammed.

Samuel’sdefiniBonofMLissBllrelevant

CSC4510‐M.A.Papalaskari‐VillanovaUniversity 16

Page 17: CSC 4510 – Machine Learningmap/4510/02MLhistory-s-u.pdf · 2012-01-26 · Machine learning • Supervised Learning – Classificaon – Regression • Unsupervised learning Others:

AcomputerprogramissaidtolearnfromexperienceEwithrespecttosometaskTandsomeperformancemeasureP,ifitsperformanceonT,asmeasuredbyP,improveswithexperienceE.

TomMitchell(1998):Well‐posedLearningProblem

CSC4510‐M.A.Papalaskari‐VillanovaUniversity 17

Page 18: CSC 4510 – Machine Learningmap/4510/02MLhistory-s-u.pdf · 2012-01-26 · Machine learning • Supervised Learning – Classificaon – Regression • Unsupervised learning Others:

DefiningtheLearningTaskImproveontask,T,withrespectto

performancemetric,P,basedonexperience,E.T:PlayingcheckersP:PercentageofgameswonagainstanarbitraryopponentE:PlayingpracBcegamesagainstitself

T:Recognizinghand‐wriYenwordsP:PercentageofwordscorrectlyclassifiedE:Databaseofhuman‐labeledimagesofhandwriYenwords

T:Drivingonfour‐lanehighwaysusingvisionsensorsP:Averagedistancetraveledbeforeahuman‐judgederrorE:Asequenceofimagesandsteeringcommandsrecordedwhileobservingahumandriver.

T:DeterminewhichstudentslikeorangesorapplesP:Percentageofstudents’preferencesguessedcorrectlyE:StudentaYributedata

CSC4510‐M.A.Papalaskari‐VillanovaUniversity 18

Page 19: CSC 4510 – Machine Learningmap/4510/02MLhistory-s-u.pdf · 2012-01-26 · Machine learning • Supervised Learning – Classificaon – Regression • Unsupervised learning Others:

DesigningaLearningSystem•  Choosethetrainingexperience•  Chooseexactlywhatistoobelearned,i.e.thetargetfunc>on.•  ChoosealearningalgorithmtoinferthetargetfuncBonfromthe

experience.•  Alearningalgorithmwillalsodetermineaperformancemeasure

Environment/Experience

Learner

Knowledge

PerformanceElement

CSC4510‐M.A.Papalaskari‐VillanovaUniversity

19

Page 20: CSC 4510 – Machine Learningmap/4510/02MLhistory-s-u.pdf · 2012-01-26 · Machine learning • Supervised Learning – Classificaon – Regression • Unsupervised learning Others:

Improveontask,T,withrespecttoperformancemetric,P,basedonexperience,E.

CSC4510‐M.A.Papalaskari‐VillanovaUniversity

Supposeyouremailprogramwatcheswhichemailsyoudoordonotmarkasspam,andbasedonthatlearnshowtobeYerfilterspam.WhatisthetaskTinthisseAng?

• Watchingyoulabelemailsasspamornotspam.• Classifyingemailsasspamornotspam• Thenumber(orfracBon)ofemailscorrectlyclassifiedasspam/notspam.• Noneoftheabove—thisisnotamachinelearningproblem.

20

Quick check:

Page 21: CSC 4510 – Machine Learningmap/4510/02MLhistory-s-u.pdf · 2012-01-26 · Machine learning • Supervised Learning – Classificaon – Regression • Unsupervised learning Others:

Machinelearning•  SupervisedLearning

– ClassificaBon– Regression

•  Unsupervisedlearning

Others:Reinforcementlearning,recommendersystems.

Alsotalkabout:PracBcaladviceforapplyinglearningalgorithms.

CSC4510‐M.A.Papalaskari‐VillanovaUniversity 21

Page 22: CSC 4510 – Machine Learningmap/4510/02MLhistory-s-u.pdf · 2012-01-26 · Machine learning • Supervised Learning – Classificaon – Regression • Unsupervised learning Others:

Machinelearning•  SupervisedLearning

– ClassificaBon– Regression

•  Unsupervisedlearning

Others:Reinforcementlearning,recommendersystems.

Alsotalkabout:PracBcaladviceforapplyinglearningalgorithms.

CSC4510‐M.A.Papalaskari‐VillanovaUniversity 22

Page 23: CSC 4510 – Machine Learningmap/4510/02MLhistory-s-u.pdf · 2012-01-26 · Machine learning • Supervised Learning – Classificaon – Regression • Unsupervised learning Others:

ClassificaBon

•  Example:Creditscoring

•  DifferenBaBngbetweenlow‐riskandhigh‐riskcustomersfromtheirincomeandsavings

Discriminant:IFincome>θ1ANDsavings>θ2 THENlow‐riskELSEhigh‐risk

23CSC4510‐M.A.Papalaskari‐VillanovaUniversity

Page 24: CSC 4510 – Machine Learningmap/4510/02MLhistory-s-u.pdf · 2012-01-26 · Machine learning • Supervised Learning – Classificaon – Regression • Unsupervised learning Others:

ClassificaBon•  Example:Irisdata•  4aYributes

–  sepallength–  sepalwidth–  petallength–  petalwidth

•  DifferenBaBngbetween3differenttypesofiris

24CSC4510‐M.A.Papalaskari‐VillanovaUniversity

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25CSC4510‐M.A.Papalaskari‐VillanovaUniversity

IrisDatamoreplots:

Page 26: CSC 4510 – Machine Learningmap/4510/02MLhistory-s-u.pdf · 2012-01-26 · Machine learning • Supervised Learning – Classificaon – Regression • Unsupervised learning Others:

ClassificaBonTree

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Page 27: CSC 4510 – Machine Learningmap/4510/02MLhistory-s-u.pdf · 2012-01-26 · Machine learning • Supervised Learning – Classificaon – Regression • Unsupervised learning Others:

FaceRecogniBon

Training examples of a person

Test images

ORL dataset, AT&T Laboratories, Cambridge UK

27CSC4510‐M.A.Papalaskari‐VillanovaUniversity

Page 28: CSC 4510 – Machine Learningmap/4510/02MLhistory-s-u.pdf · 2012-01-26 · Machine learning • Supervised Learning – Classificaon – Regression • Unsupervised learning Others:

0

100

200

300

400

0 500 1000 1500 2000 2500

HousingpricepredicBon.

Price ($) in 1000’s

Size in feet2

Regression:PredictconBnuousvaluedoutput(price)

SupervisedLearning

“rightanswers”given

28CSC4510‐M.A.Papalaskari‐VillanovaUniversity

Page 29: CSC 4510 – Machine Learningmap/4510/02MLhistory-s-u.pdf · 2012-01-26 · Machine learning • Supervised Learning – Classificaon – Regression • Unsupervised learning Others:

Machinelearning•  SupervisedLearning

– ClassificaBon– Regression

•  Unsupervisedlearning

Others:Reinforcementlearning,recommendersystems.

Alsotalkabout:PracBcaladviceforapplyinglearningalgorithms.

CSC4510‐M.A.Papalaskari‐VillanovaUniversity 29

Page 30: CSC 4510 – Machine Learningmap/4510/02MLhistory-s-u.pdf · 2012-01-26 · Machine learning • Supervised Learning – Classificaon – Regression • Unsupervised learning Others:

Regression

•  Example:Priceofausedcar

•  x:caraYributesy:price

y=g(x|θ )

g()model,

θ parameters

y=wx+w0

30

CSC4510‐M.A.Papalaskari‐VillanovaUniversity

Page 31: CSC 4510 – Machine Learningmap/4510/02MLhistory-s-u.pdf · 2012-01-26 · Machine learning • Supervised Learning – Classificaon – Regression • Unsupervised learning Others:

RegressionApplicaBons

•  NavigaBngacar:Angleofthesteering•  KinemaBcsofarobotarm

31CSC4510‐M.A.Papalaskari‐VillanovaUniversity

Page 32: CSC 4510 – Machine Learningmap/4510/02MLhistory-s-u.pdf · 2012-01-26 · Machine learning • Supervised Learning – Classificaon – Regression • Unsupervised learning Others:

SupervisedLearning:Uses•  PredicBonoffuturecases:Usetheruletopredicttheoutputforfutureinputs

•  KnowledgeextracBon:Theruleiseasytounderstand

•  Compression:Theruleissimplerthanthedataitexplains

•  OutlierdetecBon:ExcepBonsthatarenotcoveredbytherule,e.g.,fraud

32CSC4510‐M.A.Papalaskari‐VillanovaUniversity

Page 33: CSC 4510 – Machine Learningmap/4510/02MLhistory-s-u.pdf · 2012-01-26 · Machine learning • Supervised Learning – Classificaon – Regression • Unsupervised learning Others:

• Treat both as classification problems.

• Treat problem 1 as a classification problem, problem 2 as a regression problem. • Treat problem 1 as a regression problem, problem 2 as a classification problem. • Treat both as regression problems.

You’re running a company, and you want to develop learning algorithms to address each of two problems.

Problem 1: You have a large inventory of identical items. You want to predict how many of these items will sell over the next 3 months. Problem 2: You’d like software to examine individual customer accounts, and for each account decide if it has been hacked/compromised.

Should you treat these as classification or as regression problems?

Quick check:

Page 34: CSC 4510 – Machine Learningmap/4510/02MLhistory-s-u.pdf · 2012-01-26 · Machine learning • Supervised Learning – Classificaon – Regression • Unsupervised learning Others:

Machinelearning•  SupervisedLearning

– ClassificaBon– Regression

•  Unsupervisedlearning

Others:Reinforcementlearning,recommendersystems.

Alsotalkabout:PracBcaladviceforapplyinglearningalgorithms.

CSC4510‐M.A.Papalaskari‐VillanovaUniversity 34

Page 35: CSC 4510 – Machine Learningmap/4510/02MLhistory-s-u.pdf · 2012-01-26 · Machine learning • Supervised Learning – Classificaon – Regression • Unsupervised learning Others:

x1

x2

Supervised Learning

35CSC4510‐M.A.Papalaskari‐VillanovaUniversity

Page 36: CSC 4510 – Machine Learningmap/4510/02MLhistory-s-u.pdf · 2012-01-26 · Machine learning • Supervised Learning – Classificaon – Regression • Unsupervised learning Others:

x1

x2

Unsupervised Learning

36CSC4510‐M.A.Papalaskari‐VillanovaUniversity

Page 37: CSC 4510 – Machine Learningmap/4510/02MLhistory-s-u.pdf · 2012-01-26 · Machine learning • Supervised Learning – Classificaon – Regression • Unsupervised learning Others:

UnsupervisedLearning

•  Learning“whatnormallyhappens”•  Nooutput•  Clustering:Groupingsimilarinstances

•  ExampleapplicaBons–  CustomersegmentaBon–  Imagecompression:ColorquanBzaBon

–  BioinformaBcs:LearningmoBfs

37CSC4510‐M.A.Papalaskari‐VillanovaUniversity

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38CSC4510‐M.A.Papalaskari‐VillanovaUniversity

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39CSC4510‐M.A.Papalaskari‐VillanovaUniversity

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40CSC4510‐M.A.Papalaskari‐VillanovaUniversity

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41CSC4510‐M.A.Papalaskari‐VillanovaUniversity

Page 42: CSC 4510 – Machine Learningmap/4510/02MLhistory-s-u.pdf · 2012-01-26 · Machine learning • Supervised Learning – Classificaon – Regression • Unsupervised learning Others:

[Source: Su-In Lee, Dana Pe’er, Aimee Dudley, George Church, Daphne Koller]

Gen

es

Individuals

42CSC4510‐M.A.Papalaskari‐VillanovaUniversity

Page 43: CSC 4510 – Machine Learningmap/4510/02MLhistory-s-u.pdf · 2012-01-26 · Machine learning • Supervised Learning – Classificaon – Regression • Unsupervised learning Others:

Organize computing clusters Social network analysis

Image credit: NASA/JPL-Caltech/E. Churchwell (Univ. of Wisconsin, Madison)

Astronomical data analysis Market segmentation 43CSC4510‐M.A.Papalaskari‐VillanovaUniversity

Page 44: CSC 4510 – Machine Learningmap/4510/02MLhistory-s-u.pdf · 2012-01-26 · Machine learning • Supervised Learning – Classificaon – Regression • Unsupervised learning Others:

Ofthefollowingexamples,whichwouldyouaddressusinganunsupervisedlearningalgorithm?(Checkallthatapply.)

Given a database of customer data, automatically discover market segments and group customers into different market segments.

• Given email labeled as spam/not spam, learn a spam filter.

• Given a set of web pages found on the web, automatically detect the ones that are syllabi for AI or software engineering courses • Given a dataset of patients diagnosed as either having diabetes or not, learn to classify new patients as having diabetes or not.

Quick check:

• Given a database of nutrition data, automatically discover categories of food items.

Page 45: CSC 4510 – Machine Learningmap/4510/02MLhistory-s-u.pdf · 2012-01-26 · Machine learning • Supervised Learning – Classificaon – Regression • Unsupervised learning Others:

Machinelearning•  SupervisedLearning

– ClassificaBon– Regression

•  Unsupervisedlearning

Others:Reinforcementlearning,recommendersystems.

Alsotalkabout:PracBcaladviceforapplyinglearningalgorithms.

CSC4510‐M.A.Papalaskari‐VillanovaUniversity 45

Page 46: CSC 4510 – Machine Learningmap/4510/02MLhistory-s-u.pdf · 2012-01-26 · Machine learning • Supervised Learning – Classificaon – Regression • Unsupervised learning Others:

ReinforcementLearning

•  Learningapolicy:Asequenceofoutputs•  Nosupervisedoutputbutdelayedreward•  Creditassignmentproblem

•  Gameplaying

•  Robotinamaze

•  MulBpleagents,parBalobservability,...

46CSC4510‐M.A.Papalaskari‐VillanovaUniversity

Page 47: CSC 4510 – Machine Learningmap/4510/02MLhistory-s-u.pdf · 2012-01-26 · Machine learning • Supervised Learning – Classificaon – Regression • Unsupervised learning Others:

Machinelearning•  SupervisedLearning

– ClassificaBon– Regression

•  Unsupervisedlearning

Others:Reinforcementlearning,recommendersystems.

Alsotalkabout:PracBcaladviceforapplyinglearningalgorithms.

CSC4510‐M.A.Papalaskari‐VillanovaUniversity 47

Page 48: CSC 4510 – Machine Learningmap/4510/02MLhistory-s-u.pdf · 2012-01-26 · Machine learning • Supervised Learning – Classificaon – Regression • Unsupervised learning Others:

Supervised or Unsupervised learning? Iris Data

Page 49: CSC 4510 – Machine Learningmap/4510/02MLhistory-s-u.pdf · 2012-01-26 · Machine learning • Supervised Learning – Classificaon – Regression • Unsupervised learning Others:

Summary•  MLgrewoutofworkinAI

Op>mizeaperformancecriterionusingexampledataorpastexperience.

•  Typesoflearning–  Supervised–  Unsupervised

•  RoleofStaBsBcs:Inferencefromasample

•  RoleofComputerscience:–  DatarepresentaBonandmodeling

–  EfficientalgorithmstosolveopBmizaBonproblems–  RepresenBngandevaluaBngthemodelforinference

49CSC4510‐M.A.Papalaskari‐VillanovaUniversity

Page 50: CSC 4510 – Machine Learningmap/4510/02MLhistory-s-u.pdf · 2012-01-26 · Machine learning • Supervised Learning – Classificaon – Regression • Unsupervised learning Others:

Resources:Datasets

•  UCIRepository:hYp://www.ics.uci.edu/~mlearn/MLRepository.html

•  UCIKDDArchive:hYp://kdd.ics.uci.edu/summary.data.applicaBon.html

•  Statlib:hYp://lib.stat.cmu.edu/

•  Delve:hYp://www.cs.utoronto.ca/~delve/

50CSC4510‐M.A.Papalaskari‐VillanovaUniversity

Page 51: CSC 4510 – Machine Learningmap/4510/02MLhistory-s-u.pdf · 2012-01-26 · Machine learning • Supervised Learning – Classificaon – Regression • Unsupervised learning Others:

Resources:Journals

•  JournalofMachineLearningResearchwww.jmlr.org•  MachineLearning•  NeuralComputaBon•  NeuralNetworks•  IEEETransacBonsonNeuralNetworks•  IEEETransacBonsonPaYernAnalysisandMachineIntelligence

•  AnnalsofStaBsBcs•  JournaloftheAmericanStaBsBcalAssociaBon•  ...

51CSC4510‐M.A.Papalaskari‐VillanovaUniversity

Page 52: CSC 4510 – Machine Learningmap/4510/02MLhistory-s-u.pdf · 2012-01-26 · Machine learning • Supervised Learning – Classificaon – Regression • Unsupervised learning Others:

Resources:Conferences

•  InternaBonalConferenceonMachineLearning(ICML)•  EuropeanConferenceonMachineLearning(ECML)•  NeuralInformaBonProcessingSystems(NIPS)•  UncertaintyinArBficialIntelligence(UAI)•  ComputaBonalLearningTheory(COLT)•  InternaBonalConferenceonArBficialNeuralNetworks

(ICANN)•  InternaBonalConferenceonAI&StaBsBcs(AISTATS)•  InternaBonalConferenceonPaYernRecogniBon(ICPR)•  ...

52CSC4510‐M.A.Papalaskari‐VillanovaUniversity

SomeoftheslidesinthispresentaBonareadaptedfrom:

•  Prof.FrankKlassner’sMLclassatVillanova•  theUniversityofManchesterMLcoursehYp://www.cs.manchester.ac.uk/ugt/COMP24111/

•  TheStanfordonlineMLcoursehYp://www.ml‐class.org/


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