This course introduces students to fundamental techniques, models, and ideas from statistics, machine learning, data science, and applied mathematics that underpin the new field of materials informatics. It is a more in-depth, graduate-level version of MDI 404. In addition to the topics covered above, we delve more into Bayesian models and neural networks and provide more mathematical rigor to give students a good, grounded understanding of key machine-learning models and methods.
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 calculus and linear algebra foundation and some programming experience.