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Machine Learning Presentation Outline I. What is Machine Learning? A. A subfield of AI concerned with how programs can learn from experience B. Deals with using percepts to improve an agent’s ability to act in the future C. Involves additions to an agent’s knowledge base II. Types of Learning A. From Examples 1. Good for concept-learning 2. Uses induction 3. Examples and near-misses are both important B. By Being Told 1. Rote learning 2. Inference learning C. From Experimentation 1. Improves with practice 2. Learns by discovery III. The Importance of Learning A. Helps us to understand and improve how humans learn B. Can discover new information or make predictions unable to be obtained by humans C. Can fill in incomplete (“brittle”) domains IV. Past Successes A. Oil Industry Who: British Petroleum Problem: Separating crude oil and natural gas ML used to: Determine control parameters for separation process Result: Time savings - more than a day ten minutes B. Chemical Process Control Who: Westinghouse Problem: Manufacturing nuclear fuel pellets ML used to: Determine control parameters Result: Saved $10,000,000 per year C. Loan Application Screening Who: American Express (UK) Problem: Humans had only a 50% success rate in predicting whether borderline loan applicants would default ML used to: Predict whether borderline loan applicants would default Result: Success rate went up to 70% D. Cataloging Faint Objects Who: second Palomar Observatory Sky Survey Problem: Objects in 3000 images needed to be identified and classified. Previously, this had been done by hand, but that was impossible here because there was too much data and some objects were too faint. ML used to: Develop automatic cataloging system Result: Automatic system was more than 92% accurate, which was good enough V. Current Developments A. Data mining - systematically gathering and analyzing very large amounts of information B. Medical - diagnostic systems C. Financial - targeted marketing D. Government - illegal activities, security threats VI. Current Problems A. Finding good methods of representing generalizations B. Making better use of expectations based on prior knowledge C. Dealing with inconsistencies D. Dealing with partially-learned generalizations E. Integrating logic with probability VII. The Future of Learning A. Increasing generalization of learning algorithms B. More accurate predictive abilities C. Further applications in diverse fields D. Combination with other fields of AI to form a generalpurpose, intelligent agent