Professor Joseph Nygate, PhD

Transcription

1 Professor Joseph Nygae, PhD College of Appled Scence and Technology Aprl, 2018

2 } Wha s AI and Machne Learnng ML) 10 mnues } Eample ML algorhms 15 mnues } Machne Learnng n Telecom 15 mnues } Do Machnes really Learn? 10 Mnues 2

3 } Inellgence ehbed by machnes.e. a machne mmcs cognve funcons ha humans assocae h oher human mnds } Turng Tes developed by Alan Turng n 1950, s a es of a machne's ably o ehb nellgen behavor equvalen o, or ndsngushable from, ha of a human e.g. recommend reamen for cancers, solvng problems n geomery, play chess Noe ha he ay compuer reach conclusons or decde upon acon does no have o be he ay humans do } As problems hough o requre nellgence become ell undersood, hey are removed from he defnon Opcal characer recognon neural neorks Chess playng nellgen search and prune Dsease classfcaon decson rees 3

4 } A branch of Arfcal Inellgence ha provdes sysems, he capably o learn from eperence and ac hou human nervenon or asssance } Compuers are able o learn hou beng eplcly programmed by buldng a model from sample npus } The sysems employ algorhms ha ork on he hsorcal daa o learn and mprove hemselves Daa Rules Classcal Programmng Anser Daa Anser Machne Learnng Rules 4

5 } You an o guess he oucome of ne eek's game The game ll be aay, a 9pm, and ha Joe ll play cener on offense ho ll n?? } Avalable knoledge / Arbues ha defne heher The game as home or aay Sared a 5pm, 7pm or 9pm Joe play cener or forard heher he opponen's cener as all or no.. 5

6 } The game ll be aay, a 9pm, and ha Joe ll play cener on offense ho ll n?? } Possble Ansers All games ha sar a 9, e lose Whenever Joe s cener on offense, e n Usually, hen e are aay, e n Usually, hen Fred does no sar, e lose More comple rules.. } A classfcaon problem Generalze learned rules and apply o ne eamples Need o fnd he bes se of rules ha eplans he hghes proporon of oucomes 6

7 } Gven a se of ranng cases/obecs and her arbue values, ry o deermne he arge arbue value of ne eample Classfcaon Predcon 7

8 } Wha s Arfcal Inellgence? } Wha s Machne Learnng? } Wha s Classfcaon? 8

9 } Wha s AI and Machne Learnng ML) 10 mnues } Eample ML algorhms 15 mnues } Machne Learnng n Telecom 15 mnues } Do Machnes really Learn? 10 Mnues 9

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11 } Whch arbue o use a each node Choose he mos useful arbue for classfyng he eamples } Informaon gan Measures ho ell a gven arbue separaes he ranng eamples accordng o her arge classfcaon Ths measure s used o selec among he canddae arbues a each sep hle grong he ree 11

12 Gn Inde for a gven node s = here p ) s he relave frequency of class a node ) GINI ) = 1 å [ p )] 2 12

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14 } Gn Paren) : 1 5/10 2 5/10 2 = 0.5 } Usng Var1 o spl Var1 = 1: 4 ou of 10 nsances Class = A: 1 ou of 4 and Class = B: 3 ou of 4 Gn Inde s 11/ /4 2 ) = Var1 = 0: 6 ou of 10 nsances Class= A: 4 ou of 6 and Class = B: 2 ou of 6 Gn Inde s 14/ /6 2 ) = } Gn Chldren) = 4/10 * /10 * = Class Var1 Var2 A 0 33 A 0 54 A 0 56 A 0 42 A 1 50 B 1 55 B 1 31 B 0 4 B 1 77 B

15 } Usng Var2 >=32 o spl Var2 >=32: 8 ou of 10 nsances Class = A: 5 ou of 8 and Class = B: 3 ou of 8 Gn Inde s 1 5/ /8 2 ) = Var2 < 32: 2 ou of 2 nsances Class = A: 0 ou of 2 and Class = B = 2 ou of 2 Gn Inde s 10/ /2 2 ) = 0 } Gnchldren) = 8/10 * /10 * 0 = } As he Gn Inde hen splng by Var2 >= 32 s s smaller han he Gn Inde hen splng by Var1 hch s ; e spl by Var2 >=32. } Queson: Wha ould happen f e chose Var2 >= 50? Class Var1 Var2 A 0 33 A 0 54 A 0 56 A 0 42 A 1 50 B 1 55 B 1 31 B 0 4 B 1 77 B

16 } When people buy beer hen hey ofen buy chps } When men buy dapers on Thursdays and Saurdays, hey also end o buy beer } Busness people end o buy hck mlkshakes hen hey commue o ork } The hgher he floor n offce buldng, he greaer he probably ha people ll no pay for he bagels placed n he kchen area 16

17 } Neural neorks are a ne mehod of programmng compuers and are eceponally good a performng paern recognon and oher asks ha are very dffcul o program usng convenonal echnques. } Smple uns neurons) are grouped n mulple layers and conneced ogeher usng lnks Each neuron fres hen he sum of all npus eceeds a specfc value. The eghs of each lnk and frng hreshold are learn based on a large number of ranng eamples

18 Use of a Backard propagaon algorhm ha searches for he egh values ha mnmze he oal error of he neork over he se of ranng eamples ranng se) 18 Algorhm 1. Inalze eghs 2. Read npu 3. Compue oupu 4. Updae eghs o mnmze he errors 5. Repea Some of he Formulas ha are used ) ) ) ) ) 1 ) )), 2 ) 1) ) )), 2 ) ) ) 1) 2 2 a a T T a a a a e e y W F c a a e e e e y W F E E c T T T T T T + = + = + = ø ö ç è æ + ø ö ç è æ + = = + T a e y W F E E c + = = ) )), 2 ) ) ) 1)

19 } Wha s he GINI Inde used for? } Name hree ML algorhms 19

20 } Wha s AI and Machne Learnng ML) 10 mnues } Eample ML algorhms 15 mnues } Machne Learnng n Telecom 15 mnues } Do Machnes really Learn? 10 Mnues 20

21 } Obecve successfully provsonng an cusomer order n a mely fashon e.g. home nerne, home phone, moble phone, TV channels Analyss of over 100,00 orders sho over 35% falure rae for some servces and provsonng me of over 100 days for more han 10% of he orders } Oupu Fnd paerns and rules ha predc hch orders ll succeed hou ssues, succeed bu requre manual nervenon, succeed bu ake oo long, or orders ha ll fal } Technology Decson rees Cluserng 21

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23 } One can derve rule from decson rees ha specfy hch processes and under hch crcumsances Incorrec process defnon Inerface falng Ou of scope order value Ec. If servce = EDI, LoB = Mero E, dvson = cenral or es, #lfecycles = 1 and #asks > 18 hen 85% orders compleed hou ssues f servce = EDI, LoB = Mero E, #lfecycle = #asks = 1520 and BW = Basc hen 30% of he orders ake oo long o complee If servce = EDI, BW = Basc, dvson = Norheas and #asks = 1720 hen 99% orders compleed 2982/2986) If servce = EPL, #lfecycles = LoB = Mero E, BW = basc, #asks 18 o 20 and dvson = norheas hen 45% orders had me ssues 23

24 } } Obecve Provdes cusomer care agens h nformaon on hy a cusomer s callng n Oupu nsgh hy cusomer s callng Esmaed servce resoraon rme Work Force Managemen arrval/deparure Frs Bll Confuson Bll Greaer han Las Geolocaed Servce Qualy Tcke no resolved correcly } ML Algorhms Decson rees 24

25 } Obecve Predc capacy shorfalls, deermne roo cause of performance ssues, correlae and quanfy performance of neork as a funcon of raffc } Technology decson rees, cluserng, M5Rules } Research saus ell proven echnology bu no ye appled o he neork. We have developed a prooype ha can be eended o any doman ha can be mapped o a KPI/KQI model For 99% of all he raffc f er<0.046 hen delay = < delay < 10 hen queue lenghs are < 6, b rae > 3852 and 1 < er < 10 Decson Tree Correlaon Coeffcens 25

26 } Do machnes learn n he same ay as humans? } Can hese algorhms eplan her fndngs n a ay ha s undersandable by humans? } Are machnes eplanaons smlar o a humans? } Recall ha Arfcal nellgence s a machne mmcs cognve funcons ha humans assocae h oher human mnds The Turng Tes s a es of a machne's ably o ehb nellgen behavor equvalen o, or ndsngushable from, ha of a human } So ha do you hnk, do machnes really learn? 26

27 Thank You