Published on November 26, 2008
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? Y. Abu-Mostafa 2/8
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. Y. Abu-Mostafa 3/8
Here is how Machine Learning works LEARNING PHASE: Available Machine Learning Data Algorithm OPERATIONAL PHASE: Working New Data Decision System Y. Abu-Mostafa 4/8
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: Y. Abu-Mostafa 5/8
Machine Learning in Health Care Automated White Blood Cell Classiﬁcation Collaborative project by Caltech and IRIS Corporation (US Patent # 6594586 issued July 15, 2003) Y. Abu-Mostafa 6/8
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 Y. Abu-Mostafa 7/8
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 Y. Abu-Mostafa 8/8
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