Stone, J. E., & Milner, F., & Roberts-Weigert, S. (2023, July), Full Paper: Introducing Machine Learning to First Year Engineering Students  Paper presented at 14th Annual First-Year Engineering Experience (FYEE) Conference, University of Tennessee in Knoxville, Tennessee. 10.18260/1-2--44829

\bibitem{asee_peer_44829}
  Stone, J. E., \& Milner, F., \& Roberts-Weigert, S. (2023, July), \emph{Full Paper: Introducing Machine Learning to First Year Engineering Students}
  Paper presented at 14th Annual First-Year Engineering Experience (FYEE) Conference, University of Tennessee in Knoxville, Tennessee.
  10.18260/1-2--44829

Joshua Eron Stone, Forrest Milner, and Sophie Roberts-Weigert.  "Full Paper: Introducing Machine Learning to First Year Engineering Students".  14th Annual First-Year Engineering Experience (FYEE) Conference, University of Tennessee in Knoxville, Tennessee, 2023, July.  ASEE Conferences, 2023.  https://peer.asee.org/44829 Internet.  14 Sep, 2024

\bibitem{asee_peer_44829}
  Joshua Eron Stone, Forrest Milner, and Sophie Roberts-Weigert.
  "Full Paper: Introducing Machine Learning to First Year Engineering Students".
  \emph{14th Annual First-Year Engineering Experience (FYEE) Conference, University of Tennessee in Knoxville, Tennessee, 2023, July}.
  ASEE Conferences, 2023.
  https://peer.asee.org/44829 Internet.  14 Sep, 2024

@INPROCEEDINGS{asee_peer_44829
author = "Joshua Eron Stone, Forrest Milner, and Sophie Roberts-Weigert"
title = "Full Paper: Introducing Machine Learning to First Year Engineering Students"
booktitle = "14th Annual First-Year Engineering Experience (FYEE) Conference"
year = "2023"
month = "July"
address = "University of Tennessee in Knoxville, Tennessee"
publisher = "ASEE Conferences"
note = {https://peer.asee.org/44829}
number = {10.18260/1-2--44829}
}

TY  - CPAPER
AB  - Machine learning has undoubtedly emerged as a crucial topic spanning all disciplines, particularly for those pursuing careers in technical fields. Students and instructors alike are eager to explore this innovative technology. With the increasing relevance of machine learning, it is important for students to be exposed early in their educational journey. Exploration of the topics in first year courses will help them grasp fundamental concepts more effectively and broaden their engineering horizons.
Introduction to Engineering Design (ENES100) is the University of Maryland's introductory engineering course that every engineering student is required to take in their first year in the engineering school. The course gives students a wide variety of foundational skills and knowledge useful throughout the rest of their engineering career, including 3D modeling, prototyping, manufacturing, troubleshooting, project management, coding, electronics, and teamwork.
ENES100 has piloted machine learning through the use of industry standard hardware: NVIDIA’s Jetson Nano. The Jetson Nano is used in student activities, and sewn into the fabric of the existing course by integrating the Jetson in the semester-long Over Terrain Vehicle (OTV) collaborative project. The Jetson introduces students to how machine learning works and how they can fit their own models in Python. Machine learning course material was piloted with three sections with a total of 120 students, and five teams of eight went on to develop their own machine learning classification models using the Jetson’s pre-trained image processing neural networks. Those five teams used a wireless camera to send training and testing images between their OTV and the Jetson Nano, which was loaded with their trained model. Students then used the model's feedback to perform actions on the OTV.
This paper discusses the implementation specifics, behind-the-scenes efforts, the outcomes, and student responses to the machine learning pilot program.

AU  - Joshua Eron Stone
AU  - Forrest Milner
AU  - Sophie Roberts-Weigert
CY  - University of Tennessee in Knoxville, Tennessee
DA  - 2023/07/30
PB  - ASEE Conferences
TI  - Full Paper: Introducing Machine Learning to First Year Engineering Students
UR  - https://peer.asee.org/44829
DO  - 10.18260/1-2--44829
ER  -