Courses

  1. STOR 590: Optimization for Machine Learning and Neural Networks
    • This is a special topic undergraduate course for junior and senior students, and also graduate students who expect to pursue a career in data science, machine learning, and AI.
    • The course has been taught at STOR since Spring 2022.
  2. STOR 415: Introduction to Optimization
    • This is an upper-level undergraduate course in optimization at the Department of Statistics and Operations Research
  3. STOR 612: Foundations of Optimization
    • This is a first-year graduate course, which aims at introducing the foundations of optimization to graduate students. It conveys the most important theoretical elements and methods to students who pursue research and career in different disciplines such as operations research, statistics, machine learning, computer science, engineering, applied math, and data science. The course covers both classical and modern topics from the basic to advanced theory and numerical methods. It starts from common mathematical models: linear programming and quadratic programming and moves to unconstrained convex minimization, nonconvex optimization, and ends with stochastic optimization. >> Click here to go to the course webpage.
  4. STOR 712: Optimization for Machine Learning and Data Science
    • This is an advanced optimization course for graduate students at the Department of Statistics and Operations Research