This course provides an introduction to statistical principles in the field of materials informatics. Topics covered include probability theory and modeling (frequentist/Bayesian), hypothesis testing, regression and classification analysis, dimensionality reduction, and design of experiments. Emphasis is placed on developing an understanding of statistical concepts and their specific application to materials science problems, with a focus on real-world modeling, data analysis, and best practices. Students will gain experience using Python software packages to model problems, analyze data, and interpret statistical results. Prerequisites include a solid foundation in calculus and linear algebra and some programming experience.
This course introduces students to the various interconnected techniques needed to model, learn, and control dynamic materials systems. We present these methods with the nominal motivation of experimental design: selecting experiments to run to build up a training data set or achieve a scientific objective. More broadly, however, we shall develop a toolbox of advanced algorithmic techniques to solve problems in dynamics, inference, sampling, control, and optimization.