CS 5821 - Machine Learning

The course will cover both theory and practice, applying a variety of Machine Learning techniques and models using available tools on large widely available data sets. R will be presumed, but Python and Numpy/Scipy will be used freely, as well as the natural language tools available in Python. Feature selection, model choices and relative performance measures will be presented within a Bayesian framework.

Prerequisites/Corequisites: Prerequisites: MATH 2300 and (CS 3100 or CS 3310). A grade of “C” for undergraduates and “B” for graduates needed in prerequisite courses.

Credits: 3 hours

Notes: Open to upperclass and graduate students.