Pragya Sur
STAT 195 | Spring 2026 | Course Listing | Canvas Site
Monday & Wednesday, 10:30 AM – 11:45 AM
This course is designed to follow CS 181 and will dive deeper into the statistical properties of various machine learning methods. The goal of the course is to introduce and prepare students for theoretical and methodological research in statistical machine learning. Topics we will discuss include nearest neighbors, no free lunch theorems, curse of dimensionality, structured learning, subset selection, shrinkage methods, principal components regression, optimism, effective number of parameters, cross validation, ensembling, implicit regularization and interpolation, transfer learning, algorithmic fairness, conformal inference, robustness, causal inference using machine learning. In addition to problem sets but instead of exams, students will work in groups to synthesize and give short presentations on recent applied and theoretical machine learning papers. Building upon these presentations, students will conduct individual course projects, based on which they will submit a final report at the end of the semester.
Recommended Prep: Required: CS 181, STAT 110. Recommended: STAT 111.