Washington State University

School of Electrical Engineering and Computer Science

**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, holder@eecs.wsu.edu.
Office hours: MWF 10-11, or by appointment.

- Details
- Schedule
- Lectures
- Readings
- Homework 1 (due 9/4)
- Homework 2 (due 9/18)
- Homework 3 (due 10/2)
- Homework 4 (due 10/16)
- Homework 5 (due 10/30)
- Homework 6 (due 11/13)
- Presentation
- Project