This course covers the principles underlying data mining algorithms and their applications. Algorithms that include trees and rules for classification and regression, association rules, belief networks, classical statistical models, nonlinear models such as neural networks, and local “memory-based” models are presented and examined. Examples showing how all of the preceding analysis fits together are presented. Topics include the role of metadata, how to handle missing data, and data preprocessing. Every other Spring. Credit, 3 semester hours. PREREQ: CSC 3800.