STAT 391A - Data Mining

4 Credit(s)

This course serves as an applied workshop in which students will learn modern data mining techniques to analyze large datasets. Standard techniques of data mining will be covered such as probabilistic classification, decision tree classification, neural network classification, pattern mining, sequence mining, association rules, clustering, hierarchical clustering and spectral clustering. In most cases, this course will be themed so that real data sets are taken from a specific application area (such as ecology, genetics, geology, information science, etc.) and are analyzed and/or modeled using appropriate techniques. In light of this, the techniques students use in these fields will vary from class to class.

Prerequisite(s): M 210 and STAT 3xx (Multivariate Statistics) grade B- or higher; or c/i.
(fall/odd-numbered year).