1. 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

2. 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

3. 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 general-
purpose, intelligent agent