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