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.
MINI-COURSE
NEW
Led by Distinguished Visiting Professor, Tamara Kolda
Randomized Alogorithms in Linear Algebra and Scientific Computing
The class is targeted to graduate and advanced undergraduate students, as well as faculty and postdocs.
- Overview: Success Stories for Randomized Methods; Review of Statistics and Probability, including union bounds, non-asymptotic statistics
- Review of Matrix Factorization and related concepts; Stochastic Rounding and Applications
- Randomized Range Finder and Applications
- More Randomization in Matrix Factorization
- Johnson-Lindenstrauss Transforms (JLT) and Structured Variants; Randomized Least Squares
- Randomization Applications in Optimization
EVERY Monday and Wednesday, MAY 1 - MAY 17, 2023
9:30am - 10:50am
TECH C211
This is a zero-credit course
Register via CAESAR
COMP_SCI 39957
IEMS 490: 39979
Contact Dr. Kolda at tamara.kolda@northwestern.edu for questions and topic requests
Dr. Kolda is an expert on tensor methods, data science, and optimization, and was elected in 2020 to the National Academy of Engineering. Her 2020 NAE citation reads, "For contributions to the design of scientic software, including tensor decompositions and multilinear algebra." As Distinguished Visiting Professor in the IEMS Department, Dr. Kolda is currently collaborating with Prof. Matt Plumlee on tensor methods in statistics, and with Prof. Jorge Nocedal on randomized optimization methods. After more than two decades at Sandia National Laboratories, Dr. Kolda has recently transitioned to independent consultant under her business, MathSci.ai. You can read more about Dr. Kolda and her career at https://www.MathSci.ai.
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