Machine Learning (CptS 570) Fall 2010

Section 01, MWF 9:10-10:00am, Sloan 161
Washington State University
School of Electrical Engineering and Computer Science

Past semesters taught at WSU: Fall 09, Fall 08, Fall 07

Past semesters taught (at UT Arlington): Spring 04, Fall 01, Fall 00, Fall 98, Fall 97, Fall 96

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: Ethem Alpaydin, Introduction to Machine Learning, Second Edition, MIT Press, 2010.

Grading: Six Homeworks (40%), Two Exams (20%), Project (20%), Presentation (10%), Critiques and Class Participation (10%).

Instructor: Larry Holder , EME 227, 335-6138, Office hours: MWF 10-11, or by appointment.

Course Materials

Course Resources