Data 102: Data, Inference, and Decisions

UC Berkeley, Spring 2025

Peng Ding profile photo

Peng DingInstructor

pengdingpku

Ramesh Sridharan profile photo

Ramesh SridharanInstructor

ramesh_s

Schedule

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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 26
Review MT1 Review Session
Feb 27
Midterm Midterm I
Feb 28
Lab Lab 6: GLMs and the Bootstrap (due Mar 5 at 5 PM)
March 1
Homework Homework 3 (due Mar 14 at 5 PM)

Week 7: Uncertainty Quantification and Nonparametric Methods

Mar 4
Lecture 12. Uncertainty Quantification for GLMs
Mar 5
Discussion Discussion 5 (Answers)
Mar 6
Lecture 13. Nonparametric Methods and Interpretability
Vitamin Vitamin 6 (due Mar 9 at 11:59 PM)
Mar 7
Lab Lab 7: Nonparametric methods (due Mar 12 at 5 PM)

Week 8: Neural Networks and Causal Inference

Mar 11
Lecture 14. Neural Networks and Interpretability
Mar 12
Discussion Discussion 6 (Answers)
Mar 13
Lecture 15. Causal Inference I: Association and Causation
Vitamin Vitamin 7 (due Mar 16 at 11:59 PM)
Mar 14
Lab Lab 8: Estimating Causal Effects via Instrumental Variables (due Mar 19 at 5 PM)
Mar 15
Homework Homework 4 (due Apr 4 at 5 PM)

Week 9: Causal Inference

Mar 18
Lecture 16. Causal Inference II: Randomized Experiments
Mar 19
Discussion Discussion 7 (Answers)
Mar 20
Lecture 17. Causal Inference III: Observational Studies
Vitamin Vitamin 8 (due Mar 30 at 11:59 PM)
Mar 21
Lab Lab 9: Unconfoundedness (due Apr 2 at 5 PM)

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

Week 15: 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