MCB 198 | Last taught Fall 2023
The course teaches computational, mathematical, and statistical methods for analyzing data, drawing inferences, and making predictions. It does so by building on a framework based on information theory, Bayesian statistics, and machine learning. The emphasis is on using the framework to learn how to analyze real world problems, with questions in the problem set drawn from the fields of biology, genomics, engineering, geophysics, physics and astrophysics. The framework is designed to highlight limitations of each approach, and estimate different kinds of errors, amount of data needed for the analysis, advantages and disadvantages of the different methods particularly in the context of high dimensional data, where data density is low.