Machine Learning Innovations
Yaser S. Abu-Mostafa
California Institute of Technology
Pro-Innovation Infrastructure Panel
Global Knowledge Forum
NOOR Madinah June 24, 2008

Outline
• What is machine learning?
• Where is it used?
• Why is it important?
• How do we create its infrastructure?
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What is Machine Learning?
• Machine Learning is the technology of detecting patterns in data.
• It covers data mining, pattern recognition, and neural networks.
• It has major applications in investment banking and health care.
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Here is how Machine Learning works
LEARNING PHASE:
Available Machine
Learning
Data Algorithm
OPERATIONAL PHASE:
Working
New Data Decision
System
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Examples of Machine Learning
• Finance: Credit Card approval based on historical records of customers.
• Industry: Prognosis of engine failure based on sensory measurements.
• Security: Large-scale fingerprint and biometric systems.
• The following applications in health care and investment banking:
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Machine Learning in Health Care
Automated White Blood Cell Classification
Collaborative project by Caltech and IRIS Corporation
(US Patent # 6594586 issued July 15, 2003)
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Machine Learning in Investment Banking
Automated Intraday Trading in Foreign Exchange
?
..USD EURO
10
average: without hint
average: with hint
8 3σ
Annualized Percentage Return
6
4
2
0
-2
0 50 100 150 200 250
Test Day Number
Collaborative project by Caltech and Citibank
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Why is Machine Learning important?
The explosion of data
There is vital information buried in unmanageable amount of data. Machine
Learning automates the process of extracting the information from the data.
Case in point: Data mining of medical records
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