Syllabus

This syllabus is under development and is subject to change. Due dates for homework and lab assignments will be announced later.

Week Lecture Date Topic Lecture Discussion Assignment
1 1 Tuesday 01/17/23

Course Overview, Making Decisions Under Uncertainty

2 Thursday 01/19/23

Decisions I: Binary Decision-Making

2 3 Tuesday 01/24/23

Decisions II: P-Values and Multiple Hypothesis Testing

4 Thursday 01/26/23

Decisions III: Online False Discovery Rate Control & ROC curves

3 5 Tuesday 01/31/23

Decisions IV: Frequentist vs. Bayesian Decision-Making

6 Thursday 02/02/23

Bayesian Modeling I: Overview of Bayesian Modeling

4 7 Tuesday 02/07/23

Bayesian Modeling II: Graphical Models

8 Thursday 02/09/23

Bayesian Modeling III: Rejection Sampling and Markov Chains

5 9 Tuesday 02/14/23

Bayesian Modeling IV: Markov Chain Monte Carlo (MCMC) and Gibbs Sampling

10 Thursday 02/16/23

GLMs I: Regression and GLMs

6 11 Tuesday 02/21/23

GLMs II: Model checking for GLMs

12 Thursday 02/23/23

GLMs III: Uncertainty quantification for GLMs

7 Tuesday 02/28/23

Midterm I (no lecture)

13 Thursday 03/02/23

Nonparametric Methods and Neural Networks

8 14 Tuesday 03/07/23

Neural Networks and Interpretability

15 Thursday 03/09/23

Causal Inference I: Association and Causation

9 16 Tuesday 03/14/23

Causal Inference II: Randomized Experiments

17 Thursday 03/16/23

Causal Inference III: Observational Studies

10 18 Tuesday 03/21/23

Concentration inequalities

19 Thursday 03/23/23

Bandits I

11 20 Tuesday 03/28/23

Spring break

21 Thursday 03/30/23

Spring break

12 22 Tuesday 04/04/23

Bandits II

23 Thursday 04/06/23

Reinforcement Learning 1

13 24 Tuesday 04/11/23

Reinforcement Learning 2

Thursday 04/13/23

Midterm II (no lecture)

14 25 Tuesday 04/18/23

Matching Markets and Game Theory

26 Thursday 04/20/23

Differential Privacy

15 27 Tuesday 04/25/23

Robustness and generalization

28 Thursday 04/27/23

Course Wrap-up