TY - GEN
T1 - Introducing Machine Learning in a Sophomore Signals and Systems Course
AU - Wang, Chao
AU - DIxit, Abhinav
AU - Spanias, Andreas
AU - Rao, Sunil
N1 - Funding Information:
ACKNOWLEDGEMENT Parts of the J-DSP software development were supported in part from the NSF IUSE program.
Publisher Copyright:
© 2019 IEEE.
PY - 2019/10
Y1 - 2019/10
N2 - This Innovative Practice Work in Progress Paper describes the experience and assessment of introducing machine learning concepts in a sophomore signals and systems course. Advanced machine learning concepts are typically covered in graduate level courses. However, as machine learning applications become more and more ubiquitous in our daily lives, it is important to expose students to machine learning concepts early at the undergraduate level. Signals and Systems I is a sophomore level course in the Electrical Engineering online bachelor degree curriculum. As the first course in signals and systems, it focuses on the basic concepts including signal transformation, linear time-invariant systems, Fourier series, Fourier transforms, Laplace and Z transforms. The course was taught using lecture videos and reading materials. MATLAB labs were also incorporated to introduce students to practical applications. Feedback from students showed their preference for more real world applications. This paper describes how a web-based simulation lab exercise was introduced to expose students to machine learning concepts. Specifically, students made the connection between machine learning and signals and systems concepts through a speech recognition application. In particular, students applied spectral analysis and identified voice features through pole/zero representation. To evaluate the effectiveness of the exercise, statistics from pre/post quizzes as well as student comments from a survey is analyzed.
AB - This Innovative Practice Work in Progress Paper describes the experience and assessment of introducing machine learning concepts in a sophomore signals and systems course. Advanced machine learning concepts are typically covered in graduate level courses. However, as machine learning applications become more and more ubiquitous in our daily lives, it is important to expose students to machine learning concepts early at the undergraduate level. Signals and Systems I is a sophomore level course in the Electrical Engineering online bachelor degree curriculum. As the first course in signals and systems, it focuses on the basic concepts including signal transformation, linear time-invariant systems, Fourier series, Fourier transforms, Laplace and Z transforms. The course was taught using lecture videos and reading materials. MATLAB labs were also incorporated to introduce students to practical applications. Feedback from students showed their preference for more real world applications. This paper describes how a web-based simulation lab exercise was introduced to expose students to machine learning concepts. Specifically, students made the connection between machine learning and signals and systems concepts through a speech recognition application. In particular, students applied spectral analysis and identified voice features through pole/zero representation. To evaluate the effectiveness of the exercise, statistics from pre/post quizzes as well as student comments from a survey is analyzed.
KW - Electrical Engineering
KW - Laboratory Experiences
KW - Machine Learning
KW - Online Education
KW - Signals and Systems
KW - Speech Processing Introduction
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U2 - 10.1109/FIE43999.2019.9028480
DO - 10.1109/FIE43999.2019.9028480
M3 - Conference contribution
AN - SCOPUS:85082484086
T3 - Proceedings - Frontiers in Education Conference, FIE
BT - 2019 IEEE Frontiers in Education Conference, FIE 2019
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 49th IEEE Frontiers in Education Conference, FIE 2019
Y2 - 16 October 2019 through 19 October 2019
ER -