Schedule of Classes

Introduction to the knowledge acquisition and discovery process. Cleaning and analyzing data, building machine learning models, model validation and testing, and visualization. A number of machine learning algorithms are introduced such as regression, naive Bayes, decision trees, association rules, and clustering. Feature selection and transformation. Introduction to Distributed Databases and Big Data. Programming languages, such as R and Python are covered at an accelerated pace, as the course assumes as prerequisites two semesters of programming. Emphasis is on the use of such languages for data analysis and modeling.

Individual study in an area of computer science relevant to the student's professional goals and not covered in a formal course offered by the department. May be repeated twice for a maximum of 6 credit hours.