Full course description
In this course, we will review basic concepts in statistical inference (confidence intervals, parameter estimation, and hypothesis testing). We will then study computer-intensive methods that work without imposing unrealistic or unverifiable assumptions about the data generating mechanism (randomization tests, the bootstrap, and Markov chain Monte Carlo). This will provide us with the foundations to study modern inference problems in statistics and machine learning (false discovery rates, Benjamini-Hochberg procedure, and causal inference).
Desired prior knowledge: Probability and Statistics