Course covers numerical optimization, Markov Chain Monte Carlo (MCMC), estimation-maximization (EM) algorithms, Gaussian processes, Hamiltonian Monte Carlo, statistical/machine learning, data augmentation algorithms, and techniques for dealing with missing data. Students will also be introduced to the Python programming language, and its use for statistical computing.
Prerequisites: Bios 301 or permission of instructor. Students must be familiar with the Git version control system.
Chris Fonnesbeck, PhD, Assistant Professor of Biostatistics
11137, 11th floor, 2525 West End Avenue