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 DecisionMaking



2  3  Tuesday 01/24/23 
Decisions II: PValues 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 DecisionMaking


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 Interpretability


8  14  Tuesday 03/07/23 
Neural Networks and Modeling Summary


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  Tuesday 03/28/23 
Spring break


Thursday 03/30/23 
Spring break


12  20  Tuesday 04/04/23 
Bandits II


21  Thursday 04/06/23 
Reinforcement Learning 1



13  22  Tuesday 04/11/23 
Reinforcement Learning 2


Thursday 04/13/23 
Midterm II (no lecture)



14  23  Tuesday 04/18/23 
Matching Markets


24  Thursday 04/20/23 
Differential Privacy



15  25  Tuesday 04/25/23 
Robustness and generalization


26  Thursday 04/27/23 
Course Wrapup
