Tijana Zrnic, Stanford University, Spring 2026
Mon/Wed 9:30am-10:50am, McCullough 122
TBD
This course examines the principles and methods required to make artificial intelligence (AI) systems reliable and scientifically sound. Topics include evaluation and benchmarking, notions of validity, distribution shift, predictive inference, AI-assisted statistical inference, data attribution, and beyond. Problem sets will involve both mathematical components and coding projects to see the practical effects of the methods we develop.
| Lecture | Date | Topics | Reading |
|---|---|---|---|
| 1 | Mar 30 | Benchmarks; Holdout method | TBD |
| 2 | Apr 1 | Cross-validation; Bootstrap | TBD |
| 3 | Apr 6 | Model selection; Overfitting & selection bias | TBD |
| 4 | Apr 8 | Adaptive overfitting | TBD |
| 5 | Apr 13 | Internal, external, & construct validity | TBD |
| 6 | Apr 15 | Frontier lecture | TBD |
| 7 | Apr 20 | Distribution shift | TBD |
| 8 | Apr 22 | Predictive inference; Conformal prediction | TBD |
| 9 | Apr 27 | Predictive inference under distribution shift | TBD |
| 10 | Apr 29 | Calibration | TBD |
| 11 | May 4 | Multicalibration | TBD |
| 12 | May 6 | Frontier lecture | TBD |
| 13 | May 11 | AI for science; Prediction-powered inference (PPI) | TBD |
| 14 | May 13 | PPI pt. 2 | TBD |
| 15 | May 18 | AI-assisted annotation | TBD |
| 16 | May 20 | Data attribution | TBD |
| 17 | May 25 | Data attribution pt. 2 | TBD |
| 18 | May 27 | Frontier lecture | TBD |
“Frontier lectures” will consist of student presentations of frontier papers related to the class topics.