| About this Abstract |
| Meeting |
MS&T22: Materials Science & Technology
|
| Symposium
|
Curricular Innovations and Continuous Improvement of Academic Programs (and Satisfying ABET along the Way): The Elizabeth Judson Memorial Symposium
|
| Presentation Title |
Machine Learning and Data Science in the MSE Undergraduate Program |
| Author(s) |
Elizabeth A. Holm |
| On-Site Speaker (Planned) |
Elizabeth A. Holm |
| Abstract Scope |
The tools of data science and machine learning are constructed using concepts from the mathematics and statistics core of the undergraduate engineering curriculum; our students are ready and able to learn them. Given the growing importance of these subjects in the practice of materials science and engineering, it makes sense to include data science and machine learning in the undergraduate program. At CMU, we incorporate them into the MSE curriculum via focused modules inserted into our computational materials science course; outcomes and lessons learned will be discussed. Summer research opportunities supplement course offerings; an example of a team project on machine learning for microstructural analysis will be presented and critiqued. Finally, we stress the importance of making MSE data and data science problems more available for student exploration. The ultimate goal is to provide undergraduates with multiple avenues to acquire data science and machine learning experience during their MSE education. |