CSE 446
Machine Learning
Credits
3.0
Lead Instructor
Dan Weld
Textbook
Course Description
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 CSE 326 or CSE 332; either STAT 390, STAT 391, or CSE 312.
CE Major Status
Selected Elective
Course Objectives
No data available!
ABET Outcomes
No outcomes registered
Course Topics
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