Optimization Models and Algorithms for Data Science
509005 · 6 CFU · Department of Mathematics · Archive, A.Y. 2019/2020 – 2022/2023
This page is an archive of past editions. The course is now taught together with Operations Research: see the current course page.
The Jupyter notebooks are available on GitHub at mathcoding/opt4ds.
Slides
- Introduction to Linear Programming (PDF, 0.5 MB)
- Geometry of Linear Programming (PDF, 0.3 MB)
- Linear Programming and Linear Algebra (PDF, 0.3 MB)
- The Simplex Method (PDF, 0.3 MB)
- Building LP Models: part 1 (PDF, 6.5 MB), part 2 (PDF, 0.6 MB)
- Network Flows: part 1 (PDF, 7.9 MB), part 2 (PDF, 1.3 MB)
- Mixed Integer Programming: Branch and Cut (PDF, 1.8 MB)
- The Traveling Salesman Problem (PDF, 3.0 MB)
- RNA Folding (PDF, 3.8 MB)
- Slides of the 2019/2020 edition (ZIP, 26 MB)
Readings and links
A curated list of reference books on Operations Research is available on OR Stack Exchange.
Optimization and machine learning
- TEDx talk by Marco Lübbecke on optimization (YouTube)
- Evolution of randomness in optimization methods for supervised machine learning (SIAG/OPT Views and News)
- Adversarial attacks on medical machine learning (Science, 2019)
- The softmax function and its derivative
- Runge phenomena: polynomial interpolation at evenly spaced nodes converges if and only if the function is analytic inside a football-shaped region.