All stat opt cs math Privacy-Preserving Machine Learning CSCI 699 USC Graduate Foundations of privacy-preserving machine learning, focusing on maximizing utility while protecting individual privacy against modern attacks. Causal Inference with Modern Machine Learning Methods DSO 603 USC Marshall Graduate Doctoral-level introduction to causal inference at the intersection of machine learning, focusing on theoretical foundations and recent research developments. Advanced Data Science: Modeling, Computing, & Optimization DSO 699 USC Marshall Graduate TBD Jacob Bien Ph.D. level course on statistical machine learning, covering the core process from data to model, algorithm, and insight. Optimization for the Information and Data Sciences EE 588 USC Viterbi Graduate Graduate level course on convex optimization, first-order algorithms, and their applications in data science. Applied Generative AI for Enterprises ISE-547 USC Viterbi Graduate Master's level course on Applied Generative Artificial Intelligence for Enterprises at USC. Graduate Mathematical Statistics Math 541A/B, STA6010 USC, Yonsei University Graduate Foundational sequences in graduate mathematical statistics covering probability, estimation, hypothesis testing, and asymptotics. Statistical Computing for Data Science II STA6172 Yonsei University Graduate Advanced Monte Carlo statistical methods for Bayesian data analysis and data science. Generalized Linear Mixed Models STA6640 Yonsei University Graduate Theory and data analysis methods for Generalized Linear Mixed Models (GLMM), extending regression to exponential families and random effects. Machine Learning for/with Mixed-Integer Optimization ISE 617 / CSCI 617 USC Viterbi Graduate (Doctoral) Spring 2026 Ph.D. level course exploring the intersection of Machine Learning and Mixed-Integer Optimization, covering how MIO enhances ML and how ML improves MIO solvers. Modern Statistical Inference DSO 699 USC Marshall Graduate Fall 2023 Adel Javanmard Ph.D. level course on Modern Statistical Inference, focusing on high-dimensional settings and statistical learning theory.
