The course discusses data mining and introduces students to machine learning concepts used in analytics. It provides the basics of building predictive models using structured and unstructured data and clustering, association, and classification techniques. It covers predictive modeling using regression, survival analysis, artificial neural networks, support vector machines, decision trees, and genetic algorithms. The courses involves hands-on exercises with WEKA, Python, and R.
P: MATH 260 or INFO SCI 302
Spring.