CSC 8015 - Data Mining and Predictive Analytics

3 cr.

This course provides an overview of generative and discriminative classifiers for data mining applications. Topics to be covered include decision trees, rule learners, regression models, Bayesian models, and support vector machines, as well as clustering methods such as k-means, kNN and EM. Students will also be introduced to large-scale datasets and the need for data cleaning and data preparation issues, including noise models, dealing with missing and unbalanced data, discrete versus continuous features, and feature selection, including PCA and mutual information. All methods will be implemented in standard statistical packages or extensions to languages like python.

Prerequisite(s): CSC 7500 or equivalent, MATH 1700 or equivalent, and CSC 7015 (may be taken concurrently)