Machine Learning: An Undergraduate Engineering Course

Sami Khorbotly

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Conference: 2022 ASEE Illinois-Indiana Section Conference
Location: Anderson, Indiana
Publication Date: April 9, 2022
Start Date: April 9, 2022
End Date: April 9, 2022
Page Count: 5
DOI: 10.18260/1-2--42132
Permanent URL: https://peer.asee.org/42132
Download Count: 304

Paper Authors

biography

Sami Khorbotly Valparaiso University

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Received the Bachelor of Engineering degree in Electrical Engineering from Beirut Arab University, Beirut, Lebanon in 2001. He then received the M.S. and Ph. D. degrees both in Electrical and Computer Engineering from the University of Akron, Akron, OH in 2003 and 2007, respectively. He is currently a Professor and Chair of the Department of Electrical and Computer Engineering at Valparaiso University. He teaches in the areas of digital systems and digital signal & image processing. His research interests include digital circuits design and robotic systems.

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Abstract

In today’s quickly changing world, staying up-to-date is a recipe for success. This is particularly true in the Electrical and Computer Engineering (ECE) field. While the main concepts in circuits and programming remain unchanged, the tools and the applications are changing at a very fast pace. As a result, curriculum committees within ECE programs are continuously striving to update their curricula to prepare their graduates for success in both the industrial and the research worlds. Machine learning (ML) is an ECE area that continues to gain an increasing popularity in the ECE industries. Consequently, it is becoming a staple on the list of “highly-demanded skills” for many of the ECE employers. In order to fulfill this workforce need and help increase the employability of our graduates, an undergraduate machine learning course has been developed and will be taught in Spring 2022. The course will be offered as a 3 credit upper level technical elective for Electrical Engineering, Computer Engineering, Mechanical Engineering, and Computer Science students. Several challenges were encountered when developing this course. First of all, considering the myriad of topics that can fit under the ML umbrella, it was necessary to find an adequate breadth vs depth balance to design a course that fits within the time constraints of an academic calendar. Also, considering the undergraduate nature of the course, some of the high-complexity mathematical concepts had to be simplified without sacrificing the rigor of the course. Another challenge was finding the right balance between covering the conceptual background behind the various ML methods as opposed to exclusively teaching the practical tools and techniques that allows the students to find solutions without looking “under the hood”. Finally, as numerous computing platforms can be used to implement ML systems, a thorough investigation was executed to identify the most adequate platform to use in the course. This paper will share the ins and outs of the newly developed course. It will discuss the appropriate topics to be included and the adequate amount of depth for an undergraduate audience. It will also discuss the selected computing platform with the rationale for all the decisions.

Citation
Format

Khorbotly, S. (2022, April), Machine Learning: An Undergraduate Engineering Course Paper presented at 2022 ASEE Illinois-Indiana Section Conference , Anderson, Indiana. 10.18260/1-2--42132

TY - CPAPER
AB - In today’s quickly changing world, staying up-to-date is a recipe for success. This is particularly true in the Electrical and Computer Engineering (ECE) field. While the main concepts in circuits and programming remain unchanged, the tools and the applications are changing at a very fast pace. As a result, curriculum committees within ECE programs are continuously striving to update their curricula to prepare their graduates for success in both the industrial and the research worlds.
Machine learning (ML) is an ECE area that continues to gain an increasing popularity in the ECE industries. Consequently, it is becoming a staple on the list of “highly-demanded skills” for many of the ECE employers. In order to fulfill this workforce need and help increase the employability of our graduates, an undergraduate machine learning course has been developed and will be taught in Spring 2022. The course will be offered as a 3 credit upper level technical elective for Electrical Engineering, Computer Engineering, Mechanical Engineering, and Computer Science students.
Several challenges were encountered when developing this course. First of all, considering the myriad of topics that can fit under the ML umbrella, it was necessary to find an adequate breadth vs depth balance to design a course that fits within the time constraints of an academic calendar. Also, considering the undergraduate nature of the course, some of the high-complexity mathematical concepts had to be simplified without sacrificing the rigor of the course. Another challenge was finding the right balance between covering the conceptual background behind the various ML methods as opposed to exclusively teaching the practical tools and techniques that allows the students to find solutions without looking “under the hood”. Finally, as numerous computing platforms can be used to implement ML systems, a thorough investigation was executed to identify the most adequate platform to use in the course.
This paper will share the ins and outs of the newly developed course. It will discuss the appropriate topics to be included and the adequate amount of depth for an undergraduate audience. It will also discuss the selected computing platform with the rationale for all the decisions.

AU - Sami Khorbotly
CY - Anderson, Indiana
DA - 2022/04/09
PB - ASEE Conferences
TI - Machine Learning: An Undergraduate Engineering Course
UR - https://peer.asee.org/42132
DO - 10.18260/1-2--42132
ER -