Outline Wh y Mac hine Learning What is a w elldened - - PDF document

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Outline Wh y Mac hine Learning What is a w elldened - - PDF document

Outline Wh y Mac hine Learning What is a w elldened learning problem An example learning to pla y c hec k ers What questions should w e ask ab out Mac hine Learning lecture


slide-1
SLIDE 1 Outline
  • Wh
y Mac hine Learning
  • What
is a w elldened learning problem
  • An
example learning to pla y c hec k ers
  • What
questions should w e ask ab
  • ut
Mac hine Learning
  • lecture
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Machine L e arning T Mitc hell McGra w Hill
slide-2
SLIDE 2 Wh y Mac hine Learning
  • Recen
t progress in algorithms and theory
  • Gro
wing
  • d
  • f
  • nline
data
  • Computational
p
  • w
er is a v ailable
  • Budding
industry Three nic hes for mac hine learning
  • Data
mining
  • using
historical data to impro v e decisions
  • medical
records
  • medical
kno wledge
  • Soft
w are applications w e cant program b y hand
  • autonomous
driving
  • sp
eec h recognition
  • Self
customizing programs
  • Newsreader
that learns user in terests
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SLIDE 3 T ypical Datamining T ask Data

Patient103 Patient103 Patient103 ...

time=1 time=2 time=n Age: 23 FirstPregnancy: no Anemia: no Diabetes: no PreviousPrematureBirth: no ... Elective C−Section: ? Emergency C−Section: ? Age: 23 FirstPregnancy: no Anemia: no PreviousPrematureBirth: no Diabetes: YES ... Emergency C−Section: ? Ultrasound: abnormal Elective C−Section: no Age: 23 FirstPregnancy: no Anemia: no PreviousPrematureBirth: no ... Elective C−Section: no Ultrasound: ? Diabetes: no

Emergency C−Section: Yes

Ultrasound: ?

Giv en
  • patien
t records eac h describing a pregnancy and birth
  • Eac
h patien t record con tains
  • features
Learn to predict
  • Classes
  • f
future patien ts at high risk for Emergency Cesarean Section
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SLIDE 4 Datamining Result Data

Patient103 Patient103 Patient103 ...

time=1 time=2 time=n Age: 23 FirstPregnancy: no Anemia: no Diabetes: no PreviousPrematureBirth: no ... Elective C−Section: ? Emergency C−Section: ? Age: 23 FirstPregnancy: no Anemia: no PreviousPrematureBirth: no Diabetes: YES ... Emergency C−Section: ? Ultrasound: abnormal Elective C−Section: no Age: 23 FirstPregnancy: no Anemia: no PreviousPrematureBirth: no ... Elective C−Section: no Ultrasound: ? Diabetes: no

Emergency C−Section: Yes

Ultrasound: ?

One
  • f
  • learned
rules If No previous vaginal delivery and Abnormal nd Trimester Ultrasound and Malpresentation at admission Then Probability
  • f
Emergency CSection is
  • Over
training data
  • Over
test data
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slide-5
SLIDE 5 Credit Risk Analysis Data

Customer103: Customer103: Customer103:

(time=t0) (time=t1) (time=tn)

...

... Own House: Yes Other delinquent accts: 2 Loan balance: $2,400 Income: $52k Max billing cycles late: 3 Years of credit: 9 Profitable customer?: ? ... Own House: Yes Years of credit: 9 Profitable customer?: ? ... Own House: Yes Years of credit: 9 Loan balance: $3,250 Income: ? Other delinquent accts: 2 Max billing cycles late: 4 Loan balance: $4,500 Income: ? Other delinquent accts: 3 Max billing cycles late: 6

Profitable customer?: No

Rules learned from syn thesized data If OtherDelinquentA ccoun ts
  • and
NumberDelinquent Billi ngCy cles
  • Then
ProfitableCustome r
  • No
Deny Credit Card application If OtherDelinquentA ccoun ts
  • and
Income
  • k
OR YearsofCredit
  • Then
ProfitableCustome r
  • Yes
Accept Credit Card application
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SLIDE 6 Other Prediction Problems Customer purc hase b eha vior

Customer103: Customer103: Customer103:

(time=t0) (time=t1) (time=tn)

...

... Sex: M Age: 53 Income: $50k Own House: Yes MS Products: Word Computer: 386 PC Purchase Excel?: ? ... Sex: M Age: 53 Income: $50k Own House: Yes MS Products: Word ... Sex: M Age: 53 Income: $50k Own House: Yes Purchase Excel?: ? MS Products: Word Computer: Pentium Computer: Pentium

Purchase Excel?: Yes

Customer reten tion

Customer103: Customer103:

Age: 53 Age: 53 Age: 53 Sex: M Sex: M Sex: M

Customer103:

(time=t0) (time=t1) (time=tn)

...

Income: $50k Income: $50k Income: $50k Own House: Yes Own House: Yes Own House: Yes Checking: $5k Checking: $20k Checking: $0 Savings: $15k Savings: $0 Savings: $0 ... ... Current−customer?: yes

Current−customer?: No

Current−customer?: yes

Pro cess
  • ptimization

(time=t0) (time=t1) (time=tn)

... Product72: Product72: Product72:

... Viscosity: 1.3 ... ... Viscosity: 1.3 Product underweight?: ??

Product underweight?:

Viscosity: 3.2

Yes

Fat content: 15% Stage: mix Mixing−speed: 60rpm Density: 1.1 Stage: cook Temperature: 325 Fat content: 12% Density: 1.2 Stage: cool Fan−speed: medium Fat content: 12% Spectral peak: 3200 Density: 2.8 Spectral peak: 2800 Spectral peak: 3100 Product underweight?: ??

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SLIDE 7 Problems T
  • Dicult
to Program b y Hand AL VINN P
  • merleau
driv es
  • mph
  • n
high w a ys

Sharp Left Sharp Right

4 Hidden Units 30 Output Units 30x32 Sensor Input Retina

Straight Ahead

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SLIDE 8 Soft w are that Customizes to User h ttpwwwwisewi recom
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SLIDE 9 Where Is this Headed T
  • da
y tip
  • f
the iceb erg
  • Firstgeneration
algorithms neural nets decision trees regression
  • Applied
to w ellformated database
  • Budding
industry Opp
  • rtunit
y for tomorro w enormous impact
  • Learn
across full mixedmedia data
  • Learn
across m ultiple in ternal databases plus the w eb and newsfeeds
  • Learn
b y activ e exp erimen tation
  • Learn
decisions rather than predictions
  • Cum
ulativ e lifel
  • ng
learning
  • Programm
ing languages with learning em b edded
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SLIDE 10 Relev an t Discipli nes
  • Articial
in telli gence
  • Ba
y esian metho ds
  • Computational
complexit y theory
  • Con
trol theory
  • Information
theory
  • Philosoph
y
  • Psyc
hology and neurobiology
  • Statistics
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SLIDE 11 What is the Learning Problem Learning
  • Impro
ving with exp erience at some task
  • Impro
v e
  • v
er task T
  • with
resp ect to p erformance measure P
  • based
  • n
exp erience E
  • Eg
Learn to pla y c hec k ers
  • T
  • Pla
y c hec k ers
  • P
  • f
games w
  • n
in w
  • rld
tournamen t
  • E
  • pp
  • rtunit
y to pla y against self
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SLIDE 12 Learning to Pla y Chec k ers
  • T
  • Pla
y c hec k ers
  • P
  • P
ercen t
  • f
games w
  • n
in w
  • rld
tournamen t
  • What
exp erience
  • What
exactly should b e learned
  • Ho
w shall it b e represen ted
  • What
sp ecic algorithm to learn it
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SLIDE 13 T yp e
  • f
T raining Exp erience
  • Direct
  • r
indirect
  • T
eac her
  • r
not A problem is training exp erience represen tativ e
  • f
p erformance goal
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slide-14
SLIDE 14 Cho
  • se
the T arget F unction
  • C
hooseM
  • v
e
  • B
  • ar
d
  • M
  • v
e
  • V
  • B
  • ar
d
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slide-15
SLIDE 15 P
  • ssible
Denition for T arget F unc tion V
  • if
b is a nal b
  • ard
state that is w
  • n
then V b
  • if
b is a nal b
  • ard
state that is lost then V b
  • if
b is a nal b
  • ard
state that is dra wn then V b
  • if
b is a not a nal state in the game then V b
  • V
b
  • where
b
  • is
the b est nal b
  • ard
state that can b e ac hiev ed starting from b and pla ying
  • ptimally
un til the end
  • f
the game This giv es correct v alues but is not
  • p
erational
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SLIDE 16 Cho
  • se
Represen tation for T arget F unction
  • collecti
  • n
  • f
rules
  • neural
net w
  • rk
  • p
  • lynomial
function
  • f
b
  • ard
features
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SLIDE 17 A Represen tation for Learned F unc tion w
  • w
  • bpbw
  • r
pbw
  • bk
bw
  • r
k bw
  • btbw
  • r
tb
  • bpb
n um b er
  • f
blac k pieces
  • n
b
  • ard
b
  • r
pb n um b er
  • f
red pieces
  • n
b
  • bk
b n um b er
  • f
blac k kings
  • n
b
  • r
k b n um b er
  • f
red kings
  • n
b
  • btb
n um b er
  • f
red pieces threatened b y blac k ie whic h can b e tak en
  • n
blac ks next turn
  • r
tb n um b er
  • f
blac k pieces threatened b y red
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SLIDE 18 Obtaining T raining Examples
  • V
b the true target function
  • V
b
  • the
learned function
  • V
tr ain b the training v alue One rule for estimating training v alues
  • V
tr ain b
  • V
S uccessor b
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slide-19
SLIDE 19 Cho
  • se
W eigh t T uning Rule LMS W eigh t up date rule Do rep eatedly
  • Select
a training example b at random
  • Compute
er r
  • r
b er r
  • r
b
  • V
tr ain b
  • V
b
  • F
  • r
eac h b
  • ard
feature f i
  • up
date w eigh t w i
  • w
i
  • w
i
  • c
  • f
i
  • er
r
  • r
b c is some small constan t sa y
  • to
mo derate the rate
  • f
learning
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SLIDE 20 Design Choices

Determine Target Function Determine Representation

  • f Learned Function

Determine Type

  • f Training Experience

Determine Learning Algorithm Games against self Games against experts Table of correct moves Linear function

  • f six features

Artificial neural network Polynomial Gradient descent Board ➝ value Board ➝ move Completed Design

... ...

Linear programming

... ...

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slide-21
SLIDE 21 Some Issues in Mac hine Learning
  • What
algorithms can appro ximate functions w ell and when
  • Ho
w do es n um b er
  • f
training examples inuence accuracy
  • Ho
w do es complexit y
  • f
h yp
  • thesis
represen tation impact it
  • Ho
w do es noisy data inuence accuracy
  • What
are the theoretical limits
  • f
learnabilit y
  • Ho
w can prior kno wledge
  • f
learner help
  • What
clues can w e get from biological learning systems
  • Ho
w can systems alter their
  • wn
represen tations
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