CSE 546
Machine Learning
Credits
4.0
Lead Instructor
Luke Zettlemoyer
Textbook
None
Course Description
Explores methods for designing systems that learn from data and improve with experience. Supervised learning and predictive modeling; decision trees, rule induction, nearest neighbors, Bayesian methods, neural networks, support vector machines, and model ensembles. Unsupervised learning and clustering.
Prerequisites
either STAT 341, STAT 391, or equivalent, or permission of instructor.
CE Major Status
None
Course Objectives
none
ABET Outcomes
No outcomes registered
Course Topics