Data Science in Materials Science

4 credits

Comprehensive introduction into the application of data science to materials science. Introduction to broad array of machine learning techniques (e.g., supervised, unsupervised) and applications (e.g., regression, dimensionality reduction, classification), with a focus on practical examples in materials science. Prerequisites: graduate standing. Students may not receive credit for both NANO 281 and NANO 281R.