Data 102: Data, Inference, and Decisions

UC Berkeley, Fall 2023

Aditya Guntuboyina

Aditya Guntuboyina

aditya@stat.berkeley.edu

Ramesh Sridharan

Ramesh Sridharan

ramesh_s@berkeley.edu

Week 6

Sep 25
  • 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

Jump to current week

Week 1: Introductions

Aug 24
Lecture 1 Course Overview
Vitamin Vitamin 1 (due Aug 27 Aug 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 6 Sep 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