Approximate Class Schedule

CS 572 - Winter 1999 - Prof. Martinez
 
 
 
 
Topic Reading, approximate lectures, and Assignments
Introduction and Motivation Chapter 1 (2)
Concept Learning, Version Space, Terms and Definitions Chapter 2 (2)
Decision Trees (C4.5) Chapter 3 (2)  C4.5 Simulation
How to do Applications, Training Data, combining models, etc.    (1-2)
How to Quantify Learning Model Accuracy Ch. 5 (2)
Instance Based Learning and Case-Based Learning Ch. 8 (2)  IBL Simulation
Genetic Algorithms and Evolutionary Approaches Ch. 9 (2)
Rule Induction and Inductive Logic Programming Ch. 10 (2) CN2 Simulation
Reinforcement Learning Ch. 13 (2)
Unsupervised Learning, Incremental Concept Formation (2)
Oral Presentations (2)
Final Exam Saturday, Apr. 17 - 7:00am-10:00am
 
 Additional Topics (Time Allowing)
 
Computational Learning Theory Ch. 7
Bayesian Learning Ch. 6
Combining Induction, Analytical Learning, and prior knowledge Chs. 11,12
Latest BYU Research in Machine Learning