WANLISS, J. (2024, June), Leveraging Novel Machine Learning in Engineering Education  Paper presented at 2024 ASEE Annual Conference & Exposition, Portland, Oregon. https://peer.asee.org/47741

\bibitem{asee_peer_47741}
  WANLISS, J. (2024, June), \emph{Leveraging Novel Machine Learning in Engineering Education}
  Paper presented at 2024 ASEE Annual Conference \& Exposition, Portland, Oregon.
  https://peer.asee.org/47741

JAMES WANLISS.  "Leveraging Novel Machine Learning in Engineering Education".  2024 ASEE Annual Conference & Exposition, Portland, Oregon, 2024, June.  ASEE Conferences, 2024.  https://peer.asee.org/47741 Internet.  30 Jul, 2024

\bibitem{asee_peer_47741}
  JAMES WANLISS.
  "Leveraging Novel Machine Learning in Engineering Education".
  \emph{2024 ASEE Annual Conference \& Exposition, Portland, Oregon, 2024, June}.
  ASEE Conferences, 2024.
  https://peer.asee.org/47741 Internet.  30 Jul, 2024

@INPROCEEDINGS{asee_peer_47741
author = "JAMES WANLISS"
title = "Leveraging Novel Machine Learning in Engineering Education"
booktitle = "2024 ASEE Annual Conference \& Exposition"
year = "2024"
month = "June"
address = "Portland, Oregon"
publisher = "ASEE Conferences"
note = {https://peer.asee.org/47741}
}

TY  - CPAPER
AB  - It is vital to guarantee that engineering graduates have learned essential skills required to excel in a dynamic technological landscape. Today the proliferation of low-cost, high-speed computing devices offer opportunities for design and control of systems with varying levels of complexity. What this means in practice is that engineers increasingly need expert knowledge of various computer systems and software. Computing expertise once considered arcane must now become commonplace. We develop a novel Machine Learning (ML) course, designed for all undergraduate engineering majors with appropriate programming and mathematics background, to take as an elective in their junior or senior year. The course introduces deep learning and artificial intelligence (AI) as a basic tool engineers need to understand and utilize, even in an undergraduate engineering setting. Our paper shows how this course can be implemented in a new College of Engineering. The course uses the PyTorch machine learning framework as focus to guide students from basic ML concepts to the full deployment of models relevant to different areas of engineering.
AU  - JAMES WANLISS
CY  - Portland, Oregon
DA  - 2024/06/23
PB  - ASEE Conferences
TI  - Leveraging Novel Machine Learning in Engineering Education
UR  - https://peer.asee.org/47741
ER  -