Data 102: Data, Inference, and Decisions
UC Berkeley, Spring 2025
Peng DingInstructor
pengdingpku
Ramesh SridharanInstructor
ramesh_s
Schedule
Week 1: Binary Decisions
- Jan 21
- Lecture 1. Binary Decision-Making I
- Jan 23
- Lecture 2. Binary Decision-Making II
- Vitamin Vitamin 1 (due Jan 26 at 11:59 PM)
- Jan 24
- Lab Lab 1: Review and Warm-Up (due Jan 29 at 5 PM)
- Jan 25
- Homework Homework 1 (due Feb 7 at 5 PM)
Week 2: Multiple Testing
- Jan 28
- Lecture 3. \(p\)-Values and Multiple Hypothesis Testing
- Jan 29
- Discussion Discussion 1 (Answers)
- Jan 30
- Lecture 4. False Discovery Rate Control & ROC Curves
- Vitamin Vitamin 2 (extended to Feb 3 at 11:59 PM)
- Jan 31
- Lab Lab 2: Basics of Testing (due Feb 5 at 5 PM)
Week 3: The Bayesian Framework
- Feb 4
- Lecture 5. Frequentist vs. Bayesian Decision-Making
- Feb 5
- Discussion Discussion 2 (Answers)
- Feb 6
- Lecture 6. Introduction to Frequentist and Bayesian Modeling
- Vitamin Vitamin 3
- Feb 7
- Lab Lab 3: Loss and Risk (due Feb 12 at 5 PM)
- Homework Homework 2 (due Feb 21 at 5 PM)
Week 4: Graphical Models and Sampling
- Feb 11
- Lecture 7. Bayesian Hierarchical Models
- Feb 12
- Discussion Discussion 3 (Answers)
- Feb 13
- Lecture 8. Bayesian Inference with Sampling
- Vitamin Vitamin 4 (due Feb 16 at 11:59 PM)
- Feb 14
- Lab Lab 4: Bayesian Estimation in Hierarchical Graphical Models (due Feb 19 at 5 PM)
Week 5: Sampling and Generalized Linear Models
- Feb 18
- Lecture 9. Rejection Sampling and Gibbs Sampling
- Feb 19
- Discussion Discussion 4 (Answers)
- Feb 20
- Lecture 10. Regression and GLMs
- Vitamin Vitamin 5 (due Feb 23rd at 11:59 PM)
- Feb 21
- Lab Lab 5: Rejection Sampling, Gibbs Sampling and GLM (due Feb 26 at 5 PM)
Week 6: Generalized Linear Models and Midterm 1
- Feb 25
- Lecture 11. Model Checking for GLMs
- Feb 27
- Midterm Midterm I
Week 7: Uncertainty Quantification and Nonparametric Methods
- Mar 4
- Lecture 12. Uncertainty Quantification for GLMs
- Mar 6
- Lecture 13. Nonparametric Methods and Neural Networks
Week 8: Neural Networks and Causal Inference
- Mar 11
- Lecture 14. Neural Networks and Interpretability
- Mar 13
- Lecture 15. Causal Inference I: Association and Causation
Week 9: Causal Inference
- Mar 18
- Lecture 16. Causal Inference II: Randomized Experiments
- Mar 20
- Lecture 17. Causal Inference III: Observational Studies
Week 10: Spring Break
- Mar 25
- Spring Break
- Mar 27
- Spring Break
Week 11: Concentration and Bandits
- Apr 1
- Lecture 18. Concentration Inequalities
- Apr 3
- Lecture 19. Bandits I
Week 12: Bandits and Reinforcement Learning
- Apr 8
- Lecture 20. Bandits II
- Apr 10
- Lecture 21. Reinforcement Learning I
Week 13: Reinforcement Learning and Midterm 2
- Apr 15
- Lecture 22. Reinforcement Learning II
- Apr 17
- Midterm Midterm II
Week 14: Conformal Inference and Case Studies
- Apr 22
- Lecture 23. Conformal Inference
- Apr 24
- Lecture 24. Case Studies, Robustness, and Generalization
Week 15: Wrap-Up
- Apr 29
- Lecture 25. Bridging Technical & Ethical Perspectives on Modeling and Decisions
- May 1
- Lecture 26. Course Wrap-Up
Week 16: RRR Week
- May 6
- RRR Week
- May 8
- RRR Week
Week 17: Finals
- May 13
- Finals Week
- May 15
- Finals Week