Education
Combining coursework from a wide range of disciplines, the Center for Optimization and Statistical Learning provides an interdisciplinary approach to focus on opportunities at the intersection of optimization and machine and statistical learning.
Optimization
- Mathematical Optimization I (IEMS 450-1)
- Mathematical Optimization II (IEMS 450-2)
- Combinatorial Optimization (IEMS 452)
- Large Scale Optimization (IEMS 454)
- Convex Optimization (IEMS 458)
- Robust Optimization (IEMS 490)
- Advanced Algorithms (COMP_SCI 457)
- Statistical Learning (IEMS 490)
- Conic Programming (IEMS 490)
- Stochastic Optimization (IEMS 490)
Statistics
- Applied Mathematical Statistics (IEMS 401)
- Predictive Analytics I (IEMS 462-1)
- Predictive Analytics II (IEMS 462-2)
- Statistical Pattern Recognition (ELEC_ENG 433)
- Introduction to Statistical Theory & Methodology (STAT 420-1,2,3)
- Statistical Computing (STAT 344)
- Regression Analysis (STAT 350)
- Nonparametric Statistical Methods (STAT 352)
- Analysis of Qualitative Data (STAT 355)
- Multivariate Statistical Methods (STAT 448)
- Advanced Analysis of Qualitative Data (STAT 455)
- Introduction to Econometrics (ECON 480-1,2,3)
Machine Learning
- Machine Learning (IEMS 455)
- Machine Learning (COMP_SCI 349)
- Machine Learning: Foundations, Applications, and Algorithms (ELEC_ENG 375, 475)
- Deep Reinforcement Learning from Scratch (ELEC_ENG 395, 495)
- Machine Learning and Artificial Intelligence for Robotics (COMP_SCI 469)
- Advanced Topics on Deep Learning (COMP_SCI 496)
Other
- Modeling with Data (ESAM 495)
- Introduction to Tensor Decompositions mini-course (May 2022, led by Distinguished Visiting Professor Tamara Kolda
MINI-COURSE
Previously offered: Spring 2023
Led by Distinguished Visiting Professor, Tamara Kolda