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datasciencelecture012017-10-10

https://michalis.co/dsc17/

michael mathioudakislyon,france

Materialfor‘DataScience’CourseatGI-INSA,2017by MichaelMathioudakis islicensedundera CreativeCommonsAttribution-ShareAlike4.0InternationalLicense.

2017.10.10 michalis.co/dsc17 2

today

• courselogistics• whatisdatascience?• thedatasciencepipeline• frequenciesandprobabilities• usingprobabilities• softwareandplatforms• commonpredictiontasks

32017.10.10 michalis.co/dsc17

courselogisticslanguage:english

butletmeknowifyouneedclarificationsoraFrenchscript

coursewebsitehttps://michalis.co/dsc17/

contactemail:[email protected]

subject: dsc17 [yourtopic]

officehoursMonday8h30-10h,byappointment

2017.10.10 4michalis.co/dsc17

courselogistics

sessions6lectures,3TPs,1exam

lecturesmethodsfordatascience

TPsonceeverytwolecturesprogramminginpython

jupyter notebook,scientificpython(scipy)stack,scikit-learn

examclosedbooks,1h50


courselogistics

prerequisites

familiaritywithprobabilities,statistics&programming

materiallectures:self-contained

itisenoughtoattendtheclass&understandtheslides

TPs:youareexpectedtoconsultonlinedocumentationforpython+softwaretools


courselogistics

references

Friedman,Jerome,TrevorHastie,andRobertTibshirani. Theelementsofstatistical

learning.Springerseriesinstatistics,Secondedition,2008.

Bishop,ChristopherM. Patternrecognitionandmachinelearning.Springer,2006.

Gelman A,CarlinJB,SternHS,DunsonDB,Vehtari A,RubinDB.Bayesiandataanalysis.

BocaRaton,FL:CRCpress;2014.


abouttheinstructor

name:‘michael’other:‘michalis’,‘michail’

maîtredeconférencesteaching:GIINSA

researchlab:LIRIS,DataSciencePole

previously…finland,canada,greece


aboutyou

youname?

yourplans?


today



datascience



courses&degrees

coursera.org


books

amazon.com


news

news.google.com


jobs

linkedin.com


software

github.com

2017.10.10 17

whatisdatascience?

michalis.co/dsc17

2017.10.10 18

datasciencedata

science

bigdata

statisticalmethods

machinelearning

cloudcomputing

michalis.co/dsc17

someterms

2017.10.10 19

datasciencedata

science

physicschemistrymedicinebiology

citytrafficsocialmediawebusage

healthsensors

customerdatapurchases

financialdataindustrialprocesses

etc…

michalis.co/dsc17

what’snewmoredata,thereforemore

opportunitytoextractinsightsbettertechnology

what’snot newwestillwanttoextractinsights,

andusethemtomakepredictions,takedecisionsthe‘logic’weuseinourmethodsisthesame (statistics/probability)

automatedanalysisoncomputers

pen&paper

extractinsightsvia…

whynow?

2017.10.10 20

datasciencedata

science

michalis.co/dsc17

moreterms

‘learning’

what dowelearn?adescription ofthedata

a‘model’thattellsushowthedataaredistributed

why?tomakepredictions(orinferences/guesses) anddecisions

(notonlyaboutthefuture)


example

22

data

learning

thepatient’stemperaturehasjustexceeded40Cwesupplythemedicineandobservetheirtemperaturechangeafter2hours

ok,we‘learned’- thenwhat?predictwhathappenstotemperatureifwesupply200mg?decide minimum dosetobecertain toachieveatleast 2Ctempdrop?

candowiththemodelwithoutthedata

model


thisexampleisacaseofregression

example

23

prediction taskdigitrecognition

given ahandwrittendigit,wewantthecomputertopredict(guess)

whatnumber itrepresentsusefulforpostal&shippingbusinesses

classification

let’ssaywearegivenmanuallylabeleddatahowwouldwecreateamodel?

howwillweuseamodelfordecisiontask?


example

datacustomerinformation

prediction taskwhatwillthenextpointbe?

clusteringdensityestimation

howwouldweuseamodel?

24

income($)

yearlypurchases($)


age<3535-50>50

yourturn

giveanexampleofprediction&decisiontasksthatwouldbeofinterestwith

thefollowingdatasets


supermarket

dataforthepast5years,everyday

whatproductswereontheshelves&atwhatpricewhoboughtwhat

predictiontask(s)?decisiontasks(s)?


traffic

dataforthepast5years,everyhour

sensorsmeasuredhowmanycarspassedfrombigintersectionsinLyon

weatherconditionsinLyon

predictiontask(s)?decisiontasks(s)?


2017.10.10 28

datasciencedata

science

michalis.co/dsc17

moreterms

‘machine’learning

whydoweusemachines?

tomakelearningautomated

andefficient

bigdatacomplexmodels


example:language

30

task:completethesentencelanguageiscomplex

basicrules(syntaxandgrammar)donotsufficeforgoodpredictions

requirescomplexmodels

datamillions/billionsofsentences/queries

userfeaturessessionattributes


today



datasciencepipeline

32

learninginference/training/fitting

data model prediction decision

modelcandidates probability

howmuchwebelievethatsomethingistrue


today



probability

howmuchwebelievethatsomethingistrueGIVEN theinformationathand

34

0:nochance 1:certain

VERYIMPORTANT!

‘proposition’


probability

35

aballdropsoutofthebox

proposition: itisgreen

whatistheprobability thatthepropositionistrue

GIVEN thatthereare100balls,40ofthemgreen?

relatedterm:‘frequency’


frequencyandprobability

36

dataweinspected100brownies,12 werelabeledasbitter

relativefrequencyofbitterbrownies?0.12 or12%

whatistheprobabilitythatthenextbrownieisbitter?


yourchocolatefactoryisexperimentingwithanewrecipeforbrownies




dataweinspected2 brownies,1 waslabeledasbitter

relativefrequencyofbitterbrownies?0.50 or50%

whatistheprobabilitythatthenextbrownieisbitter?


38

weinspectedN brownies,n werelabeledasbitter

relativefrequency ofbitterbrownies?f=n/N

p:probability thatthenextbrownieisbitter?

ifN islarge,thenitmakessensethat

p≅ f



bayes rule

itfollowsfromtheproductrule

p(A |B andI)=p(B |A andI)xp(A |I)/p(B |I)

homework:explainhow

funfact:Bayesneverwroteit


assigningprobabilities

principleofindifference

ifpropositionsA1,A2,A3,…,An aremutuallyexclusive andexhaustiveandnootherinformationisgiven

thenweassignp(Ai)=1/n


today



datasciencepipeline

43

learning



assignprobabilitiestopropositions


probability::predictionwhatistheprobabilitythatadoseof300mg

dropstemperaturemorethan2C ?

44

probability oftemp.drop>2C

givendose=300mg andmodel (seefigure)

p(temp.drop>2C |dose=300mg ;model)

giveninformation

proposition

thevalueforthisprobabilityisprovidedbythemodel!2017.10.10 michalis.co/dsc17

datasciencepipeline

45

learning



assignprobabilitiestomodels


probability::learning

46

modelM1

modelM2

considermodelswheretemperaturedropsexponentiallywithdose

drop:dose-k anderroruptoεwhatistheprobabilitythattherightmodelisM1/M2/…?

probability ofmodel(k,ε)

givendata

kfrom-5to+5andεfrom-2to+2


probability::learning

47

modelM1

modelM2

p(model(k,ε) |data;kin[-5,+5],εin[-2,+2])p(M |D;I)

fromBayes’Rule,thisisproportionaltop(data |M ;I)xp(M |I)

likelihood prior

wechoosethemodelofmaximumprobabilityp (M |D;I)

(dowehaveto?)2017.10.10 michalis.co/dsc17

datasciencepipeline

48

learning




datasciencepipeline– theBayesianway

49

learning

data prediction decisionmodelcandidates

probability funfact:ittookcenturiestoarriveatthispipeline


datasciencepipeline– inpractice

50

learning


modelcandidates


1st2nd3rd 4th

6th5th

inthiscourse…

51

learning


modelcandidates


today



53

platformsandsoftware


scientificpython


scipy.orglibrariesforscientificcomputinginpython

RandMATLAB


www.r-project.org www.mathworks.com/products/matlab.html

scikit-learn

pythonMLlibraryontopofscipy stack

manygeneralMLalgorithmsstandardizedpipeline

idealforfastprototypingonmoderatedatasets


deeplearning::tensorflow

deeplearninglibrarybasedonuser-definedcomputationgraphs

forout-of-pythonoptimization


deeplearning::other

torch.chopensourcemachinelearninglibrary

scientificframework,programminglanguage(Lua)usedbyFacebookResearch

theanohttp://deeplearning.net/software/theano/

deeplearningwithefficientnumericaloperations

microsoft cognitivetoolkit(cntk)https://cntk.ai/

tensorflow alternative

kerassimplertensorflow,theano,cntk inpython


cloud::google

59

CloudMLEngine

basicallyofferstheMLpipelinewithDeepLearningmodelsimplementedinTensorflow

otherservicestrainedmodelsforotherapplications

speech,videoorimagetagging,translationhttps://cloud.google.com/products/machine-learning/

pricing:about0.5$perhour


cloud::other

amazonawsclassificationandregression

withlogisticandlinearregression

microsoft azure‘cortana intelligence’

MLpipeline


apachespark

machinelearningalgorithmsontopofSpark

iterativeoptimization


today



regression

buildmodelthatprovidesdp(Y=y |X=x;ModelM)

forreal-valuedY

regressionmethodsdifferinthesetofmodelcandidates

theyconsider

eachmethodhascorrespondingalgorithm(s)

tosearchforbestmodel

63

X

Y

partsofthedata‘features’


someregressionmethods

64

linearregressionline+error

segmentedregressionksegments+errors

multinomialregressioncurve+error

p(M |data;I)∝ p(data |M ;I)xp(M |I)thisiswheremethodsdiffer

eachmodelcomeswithitsown2017.10.10 michalis.co/dsc17

classification

buildmodelthatprovidesp(Y=y |X=x;ModelM)forcategorically-valuedY

classificationmethodsdifferinthesetofmodelcandidates

theyconsider

eachmethodhascorrespondingalgorithm(s)

tosearchforbestmodel

65whatisX andY fordigitrecognition?


supervisedandunsupervisedlearning

regressionandclassificationarecasesof‘supervised’learning

buildmodelthatprovidesp(Y=y |X=x;ModelM)

66

somedatafeaturesotherdatafeatures

buildmodelthatprovidesp(X=x,Y=y;ModelM)

‘unsupervised’learning2017.10.10 michalis.co/dsc17

unsupervisedlearning

buildmodelthatprovidesdp(X=x,Y=y;ModelM)

findstructureinthedata

67X

Y


la fin


MLpipeline

69

learning




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