Description: A detailed investigation of current machine learning theory and methodologies. Introduces the background and basics of machine learning, including representation, inductive bias and performance evaluation. Analyzes and compares different machine learning methodologies, including statistical, connectionist, symbolic and optimization. Implementations of several methods will be provided for experimentation. Current issues in machine learning research and alternative learning methods will also be examined as they relate to course topics.
Prerequisites: Artificial Intelligence I (CSE 5360) and Artificial Intelligence II (CSE 5361).
Textbook: Tom M. Mitchell, Machine Learning, McGraw-Hill, 1997.
Grading: Six Homeworks (60%), Project (20%), Presentation (10%), Critiques and Class Participation (10%).
Instructor: Larry Holder , 333 Nedderman Hall, 272-2596, firstname.lastname@example.org. Office hours: TuTh 1-2pm, or by appointment.
Teaching Assistant: Jeff Coble, email@example.com. Office hours: Wednesdays 2-5pm, 250 Nedderman Hall.