Xu, Y., & Zhao, B., & Tung, S., & Hu, H. (2023, June), Infusing Data Science into Mechanical Engineering Curriculum with Course-Specific Machine Learning Modules  Paper presented at 2023 ASEE Annual Conference & Exposition, Baltimore , Maryland. 10.18260/1-2--43958

\bibitem{asee_peer_43958}
  Xu, Y., \& Zhao, B., \& Tung, S., \& Hu, H. (2023, June), \emph{Infusing Data Science into Mechanical Engineering Curriculum with Course-Specific Machine Learning Modules}
  Paper presented at 2023 ASEE Annual Conference \& Exposition, Baltimore , Maryland.
  10.18260/1-2--43958

Yuhao Xu, Bo Zhao, Steve Tung, and Han Hu.  "Infusing Data Science into Mechanical Engineering Curriculum with Course-Specific Machine Learning Modules".  2023 ASEE Annual Conference & Exposition, Baltimore , Maryland, 2023, June.  ASEE Conferences, 2023.  https://peer.asee.org/43958 Internet.  17 May, 2024

\bibitem{asee_peer_43958}
  Yuhao Xu, Bo Zhao, Steve Tung, and Han Hu.
  "Infusing Data Science into Mechanical Engineering Curriculum with Course-Specific Machine Learning Modules".
  \emph{2023 ASEE Annual Conference \& Exposition, Baltimore , Maryland, 2023, June}.
  ASEE Conferences, 2023.
  https://peer.asee.org/43958 Internet.  17 May, 2024

@INPROCEEDINGS{asee_peer_43958
author = "Yuhao Xu, Bo Zhao, Steve Tung, and Han Hu"
title = "Infusing Data Science into Mechanical Engineering Curriculum with Course-Specific Machine Learning Modules"
booktitle = "2023 ASEE Annual Conference \& Exposition"
year = "2023"
month = "June"
address = "Baltimore , Maryland"
publisher = "ASEE Conferences"
note = {https://peer.asee.org/43958}
number = {10.18260/1-2--43958}
}

TY  - CPAPER
AB  - Recent advances in data science algorithms and libraries have made an impact on approaches and strategies for research and development in the industrial sector, which necessitates the integration of data science into engineering education. Data science courses offered by programs such as mathematics, computer science, and data science in academic institutions normally lack the implementation in solving engineering problems. We have developed a project-based technical elective course “Machine Learning for Mechanical Engineers” and offered it to undergraduate and graduate students at the University of Arkansas. While this course is received very well by the students and has led to fruitful presentations and publications, it has a low enrollment volume from undergraduate students due to its relatively high programming requirements. A more sophisticated strategy is required to equip mechanical engineering students with data science skills without disturbing the existing curriculum. Inspired by the success of computer-aided design education at the University of Arkansas and the DIFUSE program at Dartmouth College, we have developed course-specific machine learning modules to be integrated into mechanical engineering core courses rather than dedicated data science courses. This effort includes a nonparametric regression module for Computer Methods in Mechanical Engineering, a generative design module for Computer-Aided Design, a genetic algorithms module for Thermal Systems Analysis and Design, among others. Through this practice, students will practice programming and machine learning skills every semester since their sophomore year and will be ready for the project-based technical elective machine learning course.  
AU  - Yuhao Xu
AU  - Bo Zhao
AU  - Steve Tung
AU  - Han Hu
CY  - Baltimore , Maryland
DA  - 2023/06/25
PB  - ASEE Conferences
TI  - Infusing Data Science into Mechanical Engineering Curriculum with Course-Specific Machine Learning Modules
UR  - https://peer.asee.org/43958
DO  - 10.18260/1-2--43958
ER  -