Work in Progress: Designing Laboratory Work for a Novel Embedded AI Course

Mehmet Ergezer; Bryon Kucharski; Aaron Carpenter

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Conference: 2018 ASEE Annual Conference & Exposition
Location: Salt Lake City, Utah
Publication Date: June 23, 2018
Start Date: June 23, 2018
End Date: July 27, 2018
Conference Session: Topics in Computing and Information Technology-III
Tagged Division: Computing and Information Technology
Tagged Topic: Diversity
Page Count: 11
DOI: 10.18260/1-2--31280
Permanent URL: https://peer.asee.org/31280
Download Count: 567

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Paper Authors

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Mehmet Ergezer Wentworth Institute of Technology

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Mehmet Ergezer (S'06) received the B.S. and M.S. degrees in electrical and computer engineering from Youngstown State University, Youngstown, OH, USA, in 2003 and 2006, respectively. He received the D.Eng. degree in artificial intelligence from the Department of Electrical and Computer Engineering, Cleveland State University, Cleveland, OH, USA, in May 2014.

From 2003 to 2005, following his internship with U.S. Steel, he was a Graduate Assistant with Youngstown State University. In 2006, he was a Research Assistant with the Embedded Control Systems Research Laboratory, Cleveland State University, engaged in heuristic numerical optimization techniques. In 2008, he interned with the Digital Engineering Team, Philips Healthcare. In 2011, he worked on the the development of tracking algorithms for civilian aircraft as a Staff Engineer for ARCON in Waltham, MA, USA. In 2014, Dr. Ergezer joined the Research and Advanced Development signal processing team for Bose Corp. In 2017, he became an Assistant Professor for the Department of Computer Science and Networking at Wentworth Institute of Technology.

Dr. Ergezer is a member of ACM and Eta Kappa Nu.

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Bryon Kucharski Wentworth Institute of Technology

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Senior computer engineering student at Wentworth Institute of Technology

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Aaron Carpenter Wentworth Institute of Technology

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Professor Carpenter is an Assistant Professor at the Wentworth Institute of Technology. In 2012, he completed his PhD at the University of Rochester. He now focuses his efforts to further the areas of computer architecture, digital systems, cybersecurity, and computer engineering education.

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Abstract

Laboratory work is essential for students in the Science, Technology, Engineering, and Mathematics (STEM) fields. The laboratory work provides the students with practical experience of the theory and has the potential to increase enthusiasm by motivating the students to learn via experimentation. This is an important process for students as the active learning achieves positive educational results and prepares the students for real-world problems in the STEM fields.

This paper emphasizes the development of the Artificial Intelligence (AI) laboratory work for a new course in Embedded Artificial Intelligence (EAI). The course and the laboratory work are designed as an upper-level undergraduate elective to develop an AI system to run on an embedded device. It is open to all majors in the STEM fields who meet the prerequisite of a basic programming course and a linear algebra course. After an introduction to embedded programming and sensor interface, students will be introduced to machine learning and AI. During the corresponding lab sessions, the students are given a dataset to apply the theory in a sequential fashion. The laboratories start by employing traditional statistical classification algorithms, such as logistic regression, transitioning towards a deep neural network, such as a convolutional neural network (CNN). The accuracy of each model will be noted for each laboratory, starting with a lower accuracy but smaller model size for the statistical models and culminating with a relatively high accuracy with the CNN.

This paper outlines the design of the laboratories for the AI section of the EAI course, as well the feedback received during their development. The research to create and assess the AI exercises was conducted by a senior computer engineering student without any prior experience in AI. Two different forms of learning were taken into consideration: top-down approach where the student begins with a fully functional model and works backwards to understand each step of the processes and a bottom-up approach where the student begins from scratch and implements each component, working towards a fully functionally model. A comparative study of both approaches is presented from the point of view of the student. The assessment also asked the student to rate the assignment topics, to list how many hours were spent per each lab, and to propose suggestions for improvement.

Citation
Format

Ergezer, M., & Kucharski, B., & Carpenter, A. (2018, June), Work in Progress: Designing Laboratory Work for a Novel Embedded AI Course Paper presented at 2018 ASEE Annual Conference & Exposition , Salt Lake City, Utah. 10.18260/1-2--31280

TY - CPAPER
AB - Laboratory work is essential for students in the Science, Technology, Engineering, and Mathematics (STEM) fields. The laboratory work provides the students with practical experience of the theory and has the potential to increase enthusiasm by motivating the students to learn via experimentation. This is an important process for students as the active learning achieves positive educational results and prepares the students for real-world problems in the STEM fields.

This paper emphasizes the development of the Artificial Intelligence (AI) laboratory work for a new course in Embedded Artificial Intelligence (EAI). The course and the laboratory work are designed as an upper-level undergraduate elective to develop an AI system to run on an embedded device. It is open to all majors in the STEM fields who meet the prerequisite of a basic programming course and a linear algebra course. After an introduction to embedded programming and sensor interface, students will be introduced to machine learning and AI. During the corresponding lab sessions, the students are given a dataset to apply the theory in a sequential fashion. The laboratories start by employing traditional statistical classification algorithms, such as logistic regression, transitioning towards a deep neural network, such as a convolutional neural network (CNN). The accuracy of each model will be noted for each laboratory, starting with a lower accuracy but smaller model size for the statistical models and culminating with a relatively high accuracy with the CNN.

This paper outlines the design of the laboratories for the AI section of the EAI course, as well the feedback received during their development. The research to create and assess the AI exercises was conducted by a senior computer engineering student without any prior experience in AI. Two different forms of learning were taken into consideration: top-down approach where the student begins with a fully functional model and works backwards to understand each step of the processes and a bottom-up approach where the student begins from scratch and implements each component, working towards a fully functionally model. A comparative study of both approaches is presented from the point of view of the student. The assessment also asked the student to rate the assignment topics, to list how many hours were spent per each lab, and to propose suggestions for improvement.
AU - Mehmet Ergezer
AU - Bryon Kucharski
AU - Aaron Carpenter
CY - Salt Lake City, Utah
DA - 2018/06/23
PB - ASEE Conferences
TI - Work in Progress: Designing Laboratory Work for a Novel Embedded AI Course
UR - https://peer.asee.org/31280
DO - 10.18260/1-2--31280
ER -