目录：

1. Data collection
2. Data Preprocessing
3. ML model recap
4. Model Validation
5. Model Combination
6. Covariate and Concept Shift
7. Label Shift and Drift Detection
8. Data beyond IID
9. Model Tuning
10. Deep Network Tuning
11. Transfer Learning
12. Distillation
13. Multimodal data
14. Model Deployment
15. Fairness (Criteria)
16. Fairness (Fixes) and Explainability
17. Guest Lecture
18. Guest Lecture

主页：https://c.d2l.ai/stanford-cs329p/

李沐的中文版： https://space.bilibili.com/1567748478/video