# Data 102: Data, Inference, and Decisions

UC Berkeley, Fall 2024

### Alexander StrangInstructor

alexstrang

### Ramesh SridharanInstructor

ramesh_s

## Schedule

- Aug 29
**Lecture**1. Binary Decision-Making I**Vitamin**Vitamin 1 (due Sep 4 at 11:59 PM)

- Sep 2
**Lab**Lab 1: Review and Warm-Up (due Sep 4 at**5 PM**)- Sep 3
**Lecture**2. Binary Decision-Making II**Discussion**Discussion 1 (Answers)- Sep 5
**Lecture**3. Binary Decision-Making III: Hypothesis Testing**Vitamin**Vitamin 2 (due Sep 8 at 11:59 PM)- Sept 6
**Homework**Homework 1 (due Sept 20 at**5 PM**) (Answers)

- Sep 9
**Lab**Lab 2: Testing (due Sep 11 at**5 PM**)- Sep 10
**Lecture**4. Specificity and Sensitivity (Benjamini Hochberg and Neyman Pearson)**Discussion**Discussion 2 (Answers)- Sep 12
**Lecture**5. Decision Theory (Loss and Risk, Frequentist and Bayesian)**Vitamin**Vitamin 3 (due Sep 15 at 11:59 PM)

- Sep 16
**Lab**Lab 3: Loss and Risk (due Sep 18 at**5 PM**)- Sep 17
**Lecture**6. Parameter Estimation and Inference: Introduction to Frequentist and Bayesian Modeling**Discussion**Discussion 3 (Answers)- Sep 19
**Lecture**7. Bayesian Hierarchical Models**Vitamin**Vitamin 4 (due Sep 22 at 11:59 PM)- Sept 20
**Homework**Homework 2 (due Oct 4 at**5 PM**)

- Sep 23
**Lab**Lab 4: Graphical Models (due Sep 25 at**5 PM**)- Sep 24
**Lecture**8. Bayesian Hierarchical Models II**Discussion**Discussion 4 (Answers)- Sep 26
**Lecture**9. Bayesian Inference with Sampling**Vitamin**Vitamin 5 (due Sep 29 at 11:59 PM)

- Sep 30
**Lab**Lab 5: Sampling & GLMs (due Oct 2 at**5 PM**)- Oct 1
**Lecture**10. Sampling and Prediction**Discussion**Discussion 5 (Answers)- Oct 3
**Lecture**11. GLMs**Vitamin**Vitamin 6 (due Oct 6 at 11:59 PM)

- Oct 7
**Lab**Lab 5.5: GLMs (due Oct 11 at**5 PM**)- Oct 7
**Review Session and MiniLab**Slides- Oct 8
**Midterm**Midterm 1- Oct 10
**Lecture**12. Uncertainty Quantification for GLMs- Oct 11
**Homework**Homework 3 (due Oct 25 at**5 PM**)

- Oct 14
**Lab**Lab 6: GLMs and the Bootstrap (due Oct 16 at**5 PM**)- Oct 15
**Lecture**13. Nonparametric Methods and Neural Networks- Oct 17
**Lecture**14. Neural Networks and Interpretability

- Oct 22
**Lecture**15. Causal Inference I: Association and Causation- Oct 24
**Lecture**16. Causal Inference II: Randomized Experiments

- Oct 29
**Lecture**17. Causal Inference III: Observational Studies- Oct 31
**Lecture**18. Concentration Inequalities

- Nov 5
**Lecture**19. Bandits I- Nov 7
**Lecture**20. Bandits II

- Nov 12
**Lecture**21. Reinforcement Learning I- Nov 14
**Midterm**Midterm 2

- Nov 19
**Lecture**22. Reinforcement Learning II- Nov 21
**Lecture**23. Monte Carlo Tree Search

- Nov 26
**Lecture**24. Privacy in Machine Learning- Nov 28
**Lecture**Holiday

- Dec 3
**Lecture**25. Case Studies: Robustness and Generalization- Dec 5
**Lecture**26. Course Wrap-Up