teaching
Purdue University — Daniels School of Business
MGMT 590-037 · AI-Enhanced Optimization · Summer 2026
This course covers nonlinear optimization, constrained optimization, and modern AI-driven decision-making techniques. Topics include gradient-based solvers, KKT conditions, stochastic gradient descent, global optimization, and Bayesian optimization — all implemented in Python.
Course materials: github.com/pvjaiswal/AIeOpt
| # | Topic | Slides | Notebook |
|---|---|---|---|
| 1 | Nonlinear Optimization & scipy.optimize.minimize | ↓ pptx | view |
| 2 | From Prediction to Prescription — Newsvendor & SAA | view · ↓ pdf | view |
| 3 | KKT Conditions for Constrained Optimization | view · ↓ pdf | view |
| 4 | Stochastic Gradient Descent — Optimizer | view · ↓ pdf | — |
| 5 | Global Optimization — Escaping Local Minima | view · ↓ pdf | view |
| 6 | Bayesian Optimization | view · ↓ pdf | view |
| 7 | Large Language Models in Optimization | — | — |
Click view to open slides in the browser (arrow keys to navigate). Use ↓ pdf to download. Notebooks link to GitHub’s rendered viewer.