STAT 5850 - Applied Data Mining

Data mining can be described as the process of building models. For the development of models, the applied data mining course aims to go far beyond the classical statistical methods, such as linear regression. This course provides an applied overview to such modern non-linear methods as generalized additive models, decision trees, boosting, bagging and support vector machines as well as more classical linear approaches such as logistic regression, linear discriminant analysis, K-means clustering and nearest neighbors. Extensive data analyses are done using statistical programming R.

Note: Open to upper-level undergraduate and graduate students.

Prerequisites & Corequisites: Prerequisite: STAT 5680 or STAT 6620 or instructor approval

Credits: 3 hours

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