High Dimensional Data Analysis

4.0 credits

An overview of modern tools for high-dimensional data analysis, with a particular focus on connecting them to their statistical underpinnings, both applied and theoretical perspectives. Emphasis will be placed on understanding benefits and limitations of these tools. The major topics include: decision theory; basic tail and concentration bounds; univariate/multivariate methods; large-scale testing; penalized methods; dimension reduction; clustering; tree-based methods; support vector machine; network analysis.