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 57PM 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 Warmup (due Aug 30)
 Aug 29
 Lecture 2 Binary DecisionMaking
 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 DecisionMaking
 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 BetaBinomial 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 (79 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 (79 PM)
 Nov 16
 Lecture 23 Highdimensional 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: WrapUp
 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 WrapUp