Outline
Why Machine Learning?
What is a well-de ned learning problem?
An example: learning to play checkers
What questions should we ask about Machine
Learning?
1 lecture slides for textbook Machine Learning, T. Mitchell, McGraw Hill, 1997
, Why Machine Learning
Recent progress in algorithms and theory
Growing ood of online data
Computational power is available
Budding industry
Three niches for machine learning:
Data mining : using historical data to improve
decisions
{ medical records ! medical knowledge
Software applications we can't program by hand
{ autonomous driving
{ speech recognition
Self customizing programs
{ Newsreader that learns user interests
2 lecture slides for textbook Machine Learning, T. Mitchell, McGraw Hill, 1997
, Typical Datamining Task
Data:
Patient103 time=1 Patient103 time=2 ... Patient103 time=n
Age: 23 Age: 23 Age: 23
FirstPregnancy: no FirstPregnancy: no FirstPregnancy: no
Anemia: no Anemia: no Anemia: no
Diabetes: no Diabetes: YES Diabetes: no
PreviousPrematureBirth: no PreviousPrematureBirth: no PreviousPrematureBirth: no
Ultrasound: ? Ultrasound: abnormal Ultrasound: ?
Elective C−Section: ? Elective C−Section: no Elective C−Section: no
Emergency C−Section: ? Emergency C−Section: ? Emergency C−Section: Yes
... ... ...
Given:
9714 patient records, each describing a
pregnancy and birth
Each patient record contains 215 features
Learn to predict:
Classes of future patients at high risk for
Emergency Cesarean Section
3 lecture slides for textbook Machine Learning, T. Mitchell, McGraw Hill, 1997
Why Machine Learning?
What is a well-de ned learning problem?
An example: learning to play checkers
What questions should we ask about Machine
Learning?
1 lecture slides for textbook Machine Learning, T. Mitchell, McGraw Hill, 1997
, Why Machine Learning
Recent progress in algorithms and theory
Growing ood of online data
Computational power is available
Budding industry
Three niches for machine learning:
Data mining : using historical data to improve
decisions
{ medical records ! medical knowledge
Software applications we can't program by hand
{ autonomous driving
{ speech recognition
Self customizing programs
{ Newsreader that learns user interests
2 lecture slides for textbook Machine Learning, T. Mitchell, McGraw Hill, 1997
, Typical Datamining Task
Data:
Patient103 time=1 Patient103 time=2 ... Patient103 time=n
Age: 23 Age: 23 Age: 23
FirstPregnancy: no FirstPregnancy: no FirstPregnancy: no
Anemia: no Anemia: no Anemia: no
Diabetes: no Diabetes: YES Diabetes: no
PreviousPrematureBirth: no PreviousPrematureBirth: no PreviousPrematureBirth: no
Ultrasound: ? Ultrasound: abnormal Ultrasound: ?
Elective C−Section: ? Elective C−Section: no Elective C−Section: no
Emergency C−Section: ? Emergency C−Section: ? Emergency C−Section: Yes
... ... ...
Given:
9714 patient records, each describing a
pregnancy and birth
Each patient record contains 215 features
Learn to predict:
Classes of future patients at high risk for
Emergency Cesarean Section
3 lecture slides for textbook Machine Learning, T. Mitchell, McGraw Hill, 1997
