Course Synopsis

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 become proficient with the Python programming language, and its use for statistical computing.

Prerequisites

Bios 6341 (Fundamentals of Probability), Bios 6342 (Contemporary Statistical Inference), or permission of instructor. Students must be familiar with the Git version control system and be prepared to program in Python.

Instructor

Chris Fonnesbeck, PhD, Adjoint Associate Professor of Biostatistics
Principal Quantitative Analyst, Philadelphia Phillies

Teaching Assistant

TBA

Slack

All class communication will take place using Slack, a messaging system that replaces email. Students will be invited to the Bios 8366 Slack channel prior to the first week of class.

Clients for most computing and mobile platforms can be downloaded from the Slack website, or students may use the web client via a desktop browser.