Machine Learning

Machine Learning is a field at the intersection of statistics, probability, computer science, and optimization. The field is motivated by problems that are not necessarily addressed by classical statistics: how to build a face-detection system, how to design a character-recognition program, how to best display ads on webpages, how to predict movie ratings for a user. Since data for such tasks are inherently large-scale, a focus of machine learning has been on computational demands as well as on statistical accuracy. A student proficient in machine learning should therefore be versatile in both statistics and optimization. The following coursework is proposed:

 

Required:

STAT 530: Probability

STAT 531: Stochastic Processes 

STAT 550: Mathematical Statistics

ESE  504: Introduction to Optimization Theory

ESE  605: Modern Convex Optimization

CIS  520: Machine Learning

 

Optional:

STAT 542: Bayesian Methods and Computation

STAT 928: Statistical Learning Theory 

NETS 412: Algorithmic Game Theory

CIS  511: Introduction to The Theory of Computation