Sun, Z., & Peralta, L. C., & Ragins, M. A., & Chaney, N. R. (2023, June), Development of a Research-Based Course on Machine Learning and Robotics for Undergraduate Engineering Students at Hampton University  Paper presented at 2023 ASEE Annual Conference & Exposition, Baltimore , Maryland. 10.18260/1-2--43141

\bibitem{asee_peer_43141}
  Sun, Z., \& Peralta, L. C., \& Ragins, M. A., \& Chaney, N. R. (2023, June), \emph{Development of a Research-Based Course on Machine Learning and Robotics for Undergraduate Engineering Students at Hampton University}
  Paper presented at 2023 ASEE Annual Conference \& Exposition, Baltimore , Maryland.
  10.18260/1-2--43141

Zhao Sun, Laura Camila Peralta, Myles Anthony Ragins, and Niara Renee Chaney.  "Development of a Research-Based Course on Machine Learning and Robotics for Undergraduate Engineering Students at Hampton University".  2023 ASEE Annual Conference & Exposition, Baltimore , Maryland, 2023, June.  ASEE Conferences, 2023.  https://peer.asee.org/43141 Internet.  15 Aug, 2024

\bibitem{asee_peer_43141}
  Zhao Sun, Laura Camila Peralta, Myles Anthony Ragins, and Niara Renee Chaney.
  "Development of a Research-Based Course on Machine Learning and Robotics for Undergraduate Engineering Students at Hampton University".
  \emph{2023 ASEE Annual Conference \& Exposition, Baltimore , Maryland, 2023, June}.
  ASEE Conferences, 2023.
  https://peer.asee.org/43141 Internet.  15 Aug, 2024

@INPROCEEDINGS{asee_peer_43141
author = "Zhao Sun, Laura Camila Peralta, Myles Anthony Ragins, and Niara Renee Chaney"
title = "Development of a Research-Based Course on Machine Learning and Robotics for Undergraduate Engineering Students at Hampton University"
booktitle = "2023 ASEE Annual Conference \& Exposition"
year = "2023"
month = "June"
address = "Baltimore , Maryland"
publisher = "ASEE Conferences"
note = {https://peer.asee.org/43141}
number = {10.18260/1-2--43141}
}

TY  - CPAPER
AB  - Through the synergy of NASA University Leadership Initiative (ULI) Project “Safe Aviation Autonomy with Learning-enabled Components in the Loop: from Formal Assurances to Trusted Recovery Methods” and NSF Excellent in Research (EIR) project “Integrated Sensor-Robot Networks for Real-time Environmental Monitoring and Marine Ecosystem Restoration in the Hampton River”, the authors have successfully developed a research-based course on machine learning and robotics for undergraduate engineering students at Hampton University.  This paper presents the goals, challenges, design process, engaging strategies, assessment /outcomes, and lessons learned for the new course. Besides, this paper also presents the integration of IBM AI course and NVIDIA machine learning modules, along with the Couse Extension -two weeks summer undergraduate research experiences on AI/ML and robotics in the Autonomous Systems Laboratory directed by Dr. Marco Pavone at Stanford University. The success in the development of this course is due to the collaboration with Stanford University, which opening Hampton Undergraduate students' eyes to the larger issues in the area of study; due to the support from industry such as IBM and NVDIA, which provide Hampton University free training license for the online course and resources. 
AU  - Zhao Sun
AU  - Laura Camila Peralta
AU  - Myles Anthony Ragins
AU  - Niara Renee Chaney
CY  - Baltimore , Maryland
DA  - 2023/06/25
PB  - ASEE Conferences
TI  - Development of a Research-Based Course on Machine Learning and Robotics for Undergraduate Engineering Students at Hampton University
UR  - https://peer.asee.org/43141
DO  - 10.18260/1-2--43141
ER  -