CS 465 - Data Mining

This course will introduce popular data mining methods for extracting knowledge from data. It will cover the principles of data mining methods, but also provide to students hands-on experience in developing data mining solutions to scientific and business problems. Topics include: knowledge representation, data processing, machine learning and statistical methods (association mining, classification and prediction using Bayesian learning, decision trees, instance-based learning, support vector machines, neural networks, genetic algorithms, cluster analysis), evaluation of the performance and meta-learning algorithms. Ethical implications of data mining applications are considered. Applications are drawn from a variety of real life examples from different areas.MTH 432
Lecture
3 Credits
Prerequisites: CS 301 and MTH 270 or MTH 432
Offered Spring Semester Alternate Years