Syllabus

This syllabus is still under development and is subject to change.

Show all lecture descriptions

Week Lecture Date Topic Lab Discussion Homework
1 1 1/21/20

Course Overview, Making Decisions Under Uncertainty [slides]

Readings

2 1/23/20

Review of Frequentist and Bayesian Decision-Making

2 3 1/28/20

Neyman-Pearson Lemma

4 1/30/20

False Discovery Rate Control [slides]

3 5 2/4/20

Fairness in decision-making [slides]

6 2/6/20

Modeling and Regression [slides]

4 7 2/11/20

Identification Conditions for Regression [slides]

8 2/13/20

Introduction to Bayesian Modeling

5 9 2/18/20

Bayesian Hierarchical Models [slides]

10 2/20/20

Approximate Inference via Sampling I [slides]

6 11 2/25/20

Approximate Inference via Sampling II [slides]

12 2/27/20

Application of Bayesian Inference in Biology [slides]

7 13 3/3/20

Causal Inference I [slides]

14 3/5/20

Causal Inference II [slides]

  • HW 3 due
8 15 3/10/20

Rudiments of Experimental Design [slides]

16 3/12/20

Midterm

  • Midterm Review
9 17 3/17/20

Bandits: Greedy and UCB Algorithms [slides]

18 3/19/20

No Lecture

10 19 3/23/20

Spring Break

20 3/25/20

Spring Break

11 21 3/31/20

Bandits: Thompson Sampling

22 4/2/20

Time Series Modeling [slides]

12 23 4/7/20

Introduction to Reinforcement Learning

24 4/9/20

Nonparametric Methods I: Overview

13 25 4/14/20

Nonparametric Methods II: Neural Nets

26 4/16/20

Generalization, Robustness, and Distribution Shift

  • Lab 11
  • Disc 11
14 27 4/21/20

Privacy attacks and randomized response

  • HW 5 due
  • HW 6 released
28 4/23/20

Differential Privacy

  • Lab 12
  • Disc 12
15 29 4/28/20

Real-World Consequences of Decisions

30 4/30/20

TBD

  • HW 6 due
16 31 5/5/20

RRR week

32 5/7/20

RRR week

17 33 5/13/20

Final Exam