Introduces the elements of data sciences, materials informatics and data analytics in materials science and engineering. The focus will be on the applications of this emerging field for accelerated materials development. The students will also be exposed to machine learning approaches such as supervised and unsupervised learning; linear, non-linear, and logistic regression, decision trees, and artificial neural networks. They will also be trained on programming these algorithms in python and applying them for a set of case studies pertaining to structure-property relations in materials science, alloy design, additive manufacturing, and green energy technologies.
47.2 (Fall), 51.2 (Winter), 98.4 (Full Year)