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Patient103 Patient103Patient103 ...time=1 time=2 time=n

Age: 23

FirstPregnancy: noAnemia: noDiabetes: noPreviousPrematureBirth: no

...

Elective C−Section: ?Emergency C−Section: ?

Age: 23

FirstPregnancy: noAnemia: no

PreviousPrematureBirth: noDiabetes: YES

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Ultrasound: abnormal

Elective C−Section: no

Age: 23

FirstPregnancy: noAnemia: no

PreviousPrematureBirth: no

...

Elective C−Section: no

Ultrasound: ?

Diabetes: no

Emergency C−Section: Yes

Ultrasound: ?

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Patient103 Patient103Patient103 ...time=1 time=2 time=n

Age: 23

FirstPregnancy: noAnemia: noDiabetes: noPreviousPrematureBirth: no

...

Elective C−Section: ?Emergency C−Section: ?

Age: 23

FirstPregnancy: noAnemia: no

PreviousPrematureBirth: noDiabetes: YES

...Emergency C−Section: ?

Ultrasound: abnormal

Elective C−Section: no

Age: 23

FirstPregnancy: noAnemia: no

PreviousPrematureBirth: no

...

Elective C−Section: no

Ultrasound: ?

Diabetes: no

Emergency C−Section: Yes

Ultrasound: ?

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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

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Income: ?

Other delinquent accts: 3

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Age: 53

Income: $50k

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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

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Savings: $15k Savings: $0Savings: $0

... ...Current−customer?: yes Current−customer?: NoCurrent−customer?: yes

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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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Determine Representationof Learned Function

Determine Type of Training Experience

DetermineLearning Algorithm

Games against self

Games against experts Table of correct

moves

Linear functionof six features

Artificial neural network

Polynomial

Gradient descent

Board ➝ value

Board➝ move

Completed Design

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Linear programming

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