Data 102: Data, Inference, and Decisions
UC Berkeley, Fall 2023
Aditya Guntuboyina
Ramesh Sridharan
Week 6
- Lab 4 is released and due Wednesday 11:59PM.
- Homework 2 is due this Friday 5:00PM.
- Homework Party will be held Tuesday 5-7PM in Warren 101B Section A.
- Midterm 1 is next week. More details will be posted on Ed.
Schedule
Week 1: Introductions
- Aug 24
- Lecture 1 Course Overview
- Vitamin Vitamin 1 (due
Aug 27Aug 28)
Week 2: Decisions I
- Aug 28
- Lab 0 Review and Warm-up (due Aug 30)
- Aug 29
- Lecture 2 Binary Decision-Making
- Aug 30
-
- Discussion Discussion 1
- Solution
- Aug 31
- Lecture 3 \(p\)-Values and Multiple Hypothesis Testing
- Vitamin Vitamin 2 (due Sep 3)
- Sep 1
- Homework Homework 1 (PDF) (due Sep 15 5 PM)
Week 3: Decisions II
- Sep 4
- Lab 1 Basics of Testing (due
Sep 6Sep 8) - Sep 5
- Lecture 4 Online False Discovery Rate Control & ROC curves
- Sep 6
-
- Discussion Discussion 2
- Solution
- Sep 7
- Lecture 5 Frequentist vs. Bayesian Decision-Making
- Vitamin Vitamin 3 (due Sep 10)
Week 4: Bayesian Modeling I
- Sep 11
- Lab 2 Loss and Risk (due Sep 13)
- Sep 12
- Lecture 6 Overview of Bayesian Modeling
- Sep 13
-
- Discussion Discussion 3
- Solutions
- Sep 14
- Lecture 7 Beta-Binomial Inference
- Vitamin Vitamin 4 (due Sep 17)
- Sep 15
- Homework Homework 2 (PDF) (due Sep 29 5 PM)
Week 5: Bayesian Modeling II
- Sep 18
- Lab 3 Bayesian Estimation in Hierarchical Graphical Models (due Sep 20)
- Sep 19
- Lecture 8 Graphical Models, PyMC
- Sep 20
-
- Discussion Discussion 4
- Solution
- Sep 21
- Lecture 9 More PyMC, Rejection Sampling
- Vitamin Vitamin 5 (due Sep 24)
Week 6: GLM I
- Sep 25
- Lab 4 Rejection Sampling, More PyMC (due Sep 27)
- Sep 26
- Lecture 10 Regression and GLMs
- Sep 27
- Discussion Discussion 5
- Sep 28
- Lecture 11 Model checking for GLMs
- Vitamin Vitamin 6 (due Oct 1)
Week 7: GLM II
- Oct 2
- Lab 5 GLM and the Bootstrap (due Oct 4)
- Oct 3
- Lecture 12 Uncertainty quantification for GLMs
- Oct 4
- Discussion Discussion 6
- Oct 5
- Midterm Midterm 1 (7-9 PM)
- Vitamin Vitamin 7 (due Oct 8)
- Oct 6
- Homework Homework 3 (due Oct 20)
Week 8: Machine Learning
- Oct 10
- Lecture 13 Nonparametric Methods and Neural Networks
- Oct 11
- Discussion Discussion 7
- Oct 12
- Lecture 14 Neural Networks and Interpretability
- Vitamin Vitamin 8 (due Oct 15)
Week 9: Causal Inference I
- Oct 16
- Lab 6 Nonparametric Methods (due Oct 18)
- Oct 17
- Lecture 15 Association and Causation
- Oct 18
- Discussion Discussion 8
- Oct 19
- Lecture 16 Randomized Experiments
- Vitamin Vitamin 9 (due Oct 22)
- Oct 20
- Homework Homework 4 (due Nov 3)
Week 10: Causal Inference II
- Oct 23
- Lab 7 Causal Inference via Instrumental Variables (due Oct 25)
- Oct 24
- Lecture 17 Observational Studies
- Oct 25
- Discussion Discussion 9
- Oct 26
- Lecture 18 Concentration Inequalities
- Vitamin Vitamin 10 (due Oct 29)
Week 11: Bandits
- Oct 30
- Lab 8 Causal Inference via Unconfoundedness (due Nov 1)
- Oct 31
- Lecture 19 Bandits I
- Nov 1
- Discussion Discussion 10
- Nov 2
- Lecture 20 Bandits II
- Vitamin Vitamin 11 (due Nov 5)
- Nov 3
- Homework Homework 5 (due Nov 10)
Week 12: Reinforcement Learning
- Nov 6
- Lab 9 Bandits (due Nov 8)
- Nov 7
- Lecture 21 Reinforcement Learning I
- Nov 8
- Discussion Discussion 11
- Nov 9
- Lecture 22 Reinforcement Learning II
- Vitamin Vitamin 12 (due Nov 12)
Week 13: Regression revisited
- Nov 13
- Lab 10 Reinforcement Learning (due Nov 15)
- Nov 14
- Midterm Midterm 2 (7-9 PM)
- Nov 16
- Lecture 23 High-dimensional regression
- Vitamin Vitamin 13 (due Nov 19)
- Nov 17
- Homework Homework 6 (due Dec 1)
Week 14: Privacy
- Nov 21
- Lecture 24 Differential Privacy
- Nov 23
- Thanksgiving
Week 15: Wrap-Up
- Nov 27
- Lab 11 Differential Privacy (due Nov 29)
- Nov 28
- Lecture 25 Case Study: Robustness and generalization
- Nov 29
- Discussion Discussion 12
- Nov 30
- Lecture 26 Course Wrap-Up