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: Data Structures (CptS 122), Artificial Intelligence.
Textbook: Tom M. Mitchell, Machine Learning, McGraw-Hill, 1997.
Grading: Six Homeworks (40%), Two Exams (20%), Project (20%), Presentation (10%), Critiques and Class Participation (10%).
Instructor: Larry Holder , EME 227, 335-6138, firstname.lastname@example.org. Office hours: MWF 10-11, or by appointment.