MATH 466 - Advanced Big Data Analysis

This graduate level course is designed to give students a snapshot of recent techniques used to analyze, statistically and algorithmically, extremely large datasets. To accomplish this goal, the course will start with an applied and quick introduction to necessary optimization background. From there we will introduce students to topics such as spectral graph clustering, fast kernel methods, compressed sensing, among others. We will highlight applications of these methods to diverse areas such as genomics and recommender systems, but the bedrock of the course will be theory. To that end, students are expected to have a solid foundation in probability and analysis, as well as comfort with algorithmic thinking.
Units: 4
Course Type: Seminar