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Topics of Statistical Learning

Topics of Statistical Learning

Course guide

Course guide and study route
Computational workflow and reproducibility
Assignments and paper discussion
Research project
Interactive demonstrations
Reading guide

I · Weak supervision

1. Statistical targets and observation mechanisms
2. What weak supervision tells us
3. Learning from positives and unlabelled observations
4. PU estimation, overfitting, and sensitivity
5. Learning from two unlabelled mixtures

II · Preference learning

6. Pairwise preference learning
7. Contextual preferences, judges, and reward learning

III · Online learning

8. Online learning and regret
9. Bandits: learning from chosen actions
10. Duelling bandits and adaptive comparisons

IV · Inference after selection

11. Selective inference: asking a question after seeing the data
12. Selective inference after hierarchical clustering
13. Selective inference after k-means
14. Research synthesis: assumptions, validation, and independent information

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© Copyright 2026, Chi-Kuang Yeh.

Last updated on October 21, 2026.

Topics of Statistical Learning