Subject to change; check announcements and Canvas!.
| Class/Date | Class Topic | Reading Due | Assignment Due |
|---|---|---|---|
| Class 1 (Tues, Oct 24) | Introduction, course logistics | ||
| Class 2 (Thurs, Oct 26) |
Gaussian processes (Cheat sheet on multivariate Gaussians) |
Textbook Ch. 1-2 | |
| Class 3 (Tues, Oct 31) |
Gaussian processes and kernels (Jupyter demo, David Duvenaud's kernel cookbook) |
Textbook Ch. 3, 9.1 (optional) | Diagonstic problem set (TeX template and math_commands.tex macros file) |
| Class 4 (Thurs, Nov 2) | Decision theory and optimization theory | Textbook Ch. 5 | |
| Class 5 (Tues, Nov 7) (Jupyter demo on BDT acquisition functions) | Acquisition functions | Textbook Ch. 6-7 | Project proposal (for those choosing the project final asssessment option) |
| Class 6 (Thurs, Nov 9) (Jupyter demo on UCB, Jupyter demo on Thompson sampling ) | Acquisition functions | ||
| No Class on Nov 14 (Reading Week) |
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| Class 7 (Thurs, Nov 16) | Implementation details | Textbook Ch. 8, 9.2 | Short coding assignment |
| Class 8 (Tues, Nov 21) | Special Topic (Multi-Objective BO) | Multi-Objective Bayesian Optimization over High-Dimensional Search Spaces | |
| Class 9 (Thurs, Nov 23) | Special Topic (Local BO) | Scalable Global Optimization via Local Bayesian | |
| Class 10 (Tues, Nov 28) | Special Topic (Mixed-Space BO) | Bayesian Optimization over Discrete and Mixed Spaces via Probabilistic Reparameterization | |
| Class 11 (Thurs, Nov 30) | Special Topic (Causal BO) | Causal Bayesian Optimization | |
| Class 12 (Tues, Dec 5) | Special Topic (Preference BO) | Bayesian Active Learning for Classification and Preference Learning | |
| Class 13 (Thurs, Dec 7) | Special Topic (Cost-Aware BO) | Budgeted Bandit Problems with Continuous Random Costs | |