Data 102: Data, Inference, and Decisions

UC Berkeley, Fall 2024

Alexander Strang

Alexander StrangInstructor

alexstrang

Ramesh Sridharan

Ramesh SridharanInstructor

ramesh_s

Schedule

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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 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