Data, Models, and Uncertainty in the Natural Sciences

This course is for those who want to turn data into models and subsequently evaluate their uniqueness and uncertainty. Three main topics are: 1. Elementary inferential statistics, 2. Model parameter estimation via matrix inverse methods, and 3. Time series analysis and Fourier spectral density estimation. Problem sets and computer programming exercises form integral parts of the course. While the instructor's and textbook examples will be derived mostly from the physical sciences, students are encouraged to bring their own data sets for discussion. Prior programming experience in MATLAB is helpful but not required.