[PDF] - Outline Wh y Mac hine Learning What is a w elldened PDF Document

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

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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 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 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 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 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 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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