CS 451

Data Mining

Overview of techniques for gathering, exploring, transforming, modeling, and summarizing data sets including very large data sets, both structured and unstructured. Modeling approaches include techniques from supervised and unsupervised machine learning. Discussion of data cleaning and data preparation issues, including noise, missing and unbalanced data, discrete versus continuous features, and feature selection. Some techniques are implemented from scratch, while in other cases real-world tools such as R, Weka, or Python packages are applied to large-scale data sets.

Prerequisite: (MATH 230 with a minimum grade of C or MATH 202 with a minimum grade of C) and (CS 245 with a minimum grade of C)