Asee peer logo

Machine Learning: An Undergraduate Engineering Course

Download Paper |

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://strategy.asee.org/42132

Download Count

215

Request a correction

Paper Authors

biography

Sami Khorbotly Valparaiso University

visit author page

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.

visit author page

Download Paper |

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.

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

ASEE holds the copyright on this document. It may be read by the public free of charge. Authors may archive their work on personal websites or in institutional repositories with the following citation: © 2022 American Society for Engineering Education. Other scholars may excerpt or quote from these materials with the same citation. When excerpting or quoting from Conference Proceedings, authors should, in addition to noting the ASEE copyright, list all the original authors and their institutions and name the host city of the conference. - Last updated April 1, 2015