Syllabus

Course Overview, Making Decisions Under Uncertainty

• (Optional reading) Artificial Intelligence — The Revolution Hasn’t Happened Yet
• (Optional reading) What are the most important statistical ideas of the past 50 years?

Decisions I: Binary Decision-Making

• (Optional reading) The Garden of Forking Paths

Decisions II: P-Values and Multiple Hypothesis Testing

Decisions III: Online False Discovery Rate Control & ROC curves

Decisions IV: Frequentist vs. Bayesian Decision-Making

Bayesian Modeling I: Overview of Bayesian Modeling

Bayesian Modeling II: Graphical Models

Bayesian Modeling III: Rejection Sampling and Markov Chains

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

GLMs I: Regression and GLMs

GLMs II: Model checking for GLMs

GLMs III: Uncertainty quantification for GLMs

Midterm I (no lecture)

Nonparametric Methods and Interpretability

Neural Networks and Modeling Summary

• Required reading: Statistical Modeling, The Two Cultures, by Leo Breiman (pp 1-11)

Causal Inference I: Association and Causation

• Required reading for next time: Discrimination in hiring article (pdf) and skim the paper

Causal Inference II: Randomized Experiments

• Required reading: Discrimination in hiring article (pdf) and skim the paper

Causal Inference III: Observational Studies

Concentration inequalities

Bandits I

Spring break

Spring break

Bandits II

Reinforcement Learning 1

Reinforcement Learning 2

Midterm II (no lecture)

Matching Markets

Differential Privacy

Robustness and generalization

Course Wrap-up