3 Credit Hours
Theories and techniques of unsupervised learning will be covered in detail. Students will learn to find groups and other structures in unlabeled, possibly high dimensional data sets. Dimension reduction techniques for visualization and data analysis will be covered. Also, students will learn clustering, association rules, and model fitting via the EM algorithm. Other advanced topics including Support Vector Machines, k-Nearest Neighbors, Principal Component Analysis, and Naïve Bayes Classification will be studied.

Prerequisites