Covers knowledge discovery and data mining concepts, tools and methods; provides hands-on experience by requiring the coding of several non-open source algorithms and a project involving analysis of a large quantity of real-life data. Topics include the knowledge discovery process, data storage and representation issues, preprocessing algorithms of feature extraction, selection and discretization; unsupervised learning of clustering and association rules; Bayesian, inductive machine learning and neural networks (RBF) supervised learning methods; model validation methods; and data security and privacy issues. Prerequisite: graduate student standing in computer science or related discipline such as bioinformatics or acceptance into five-year accelerated program in computer science.
|3:30-4:45 PM||•||3:30-4:45 PM||•||•||•|
Dr. Gregory Triplett • Ph: (804) 828-5387 • firstname.lastname@example.org