MACHINE LEARNING IN MATERIALS
Introduction to applications of data science in materials science and engineering, including understanding the creation and use of modern data resources, data-centric approaches to materials, and the integration of machine learning across the materials landscape.
Prerequisites & course information
Requisites: Satisfied Quantitative Reasoning (QR) B, graduate/professional standing, or member of Engineering Guest Students Course Designation: Grad 50% - Counts toward 50% graduate coursework requirement Repeatable for Credit: No Last Taught: Fall 2025 Learning Outcomes: 1. Recall and utilize basic principles and methods of how informatics (data science and machine learning) can be used in materials science and engineering Audience: Both Grad & Undergrad 2. Apply software tools related to materials informatics Audience: Both Grad & Undergrad 3. Communicate effectively about technical work in oral and written presentation Audience: Both Grad & Undergrad 4. Develop machine learning workflows for informatics applications in materials science and engineering Audience: Graduate Learning Outcomes: 1. Recall and utilize basic principles and methods of how informatics (data science and machine learning) can be used in materials science and engineering Audience: Both Grad & Undergrad 2. Apply software tools related to materials informatics Audience: Both Grad & Undergrad 3. Communicate effectively about technical work in oral and written presentation Audience: Both Grad & Undergrad 4. Develop machine learning workflows for informatics applications in materials science and engineering Audience: Graduate
Sources
- Course catalogue ↗2026-2027