Data 102: Data, Inference, and Decisions

UC Berkeley, Fall 2024

Alexander StrangInstructor

alexstrang

Ramesh SridharanInstructor

ramesh_s

Schedule

Jump to current week

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)

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 2 (due Sep 15 at 11:59 PM)

Sep 17

Lecture 6. Introduction to Frequentist and Bayesian Modeling

Sep 19

Lecture 7. Bayesian Hierarchical Models

Sep 24

Lecture 8. Bayesian Inference with Sampling

Sep 26

Lecture 9. Rejection Sampling and Gibbs Sampling

Oct 1

Lecture 10. Regression and GLMs

Oct 3

Lecture 11. Model Checking for GLMs

Oct 8

Lecture Midterm 1

Oct 10

Lecture 12. Uncertainty Quantification for GLMs

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

Lecture 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