Putting the "Machine" Back in Machine Learning for Engineering Students

Rudy Chin, Dimitrios Stamoulis, Diana Marculescu
Proceedings of the Second Teaching Machine Learning and Artificial Intelligence Workshop, PMLR 170:78-82, 2022.

Abstract

Computer hardware architecture has played an important role in the recent advances made in deep learning and associated applications. However, effective teaching strategies for hardware architectures for machine learning require a different structure and technical background than classic machine learning. More specifically, not only does the material need to convey necessary machine learning concepts to students, but also covers the hardware and software infrastructure concepts required for supporting machine learning systems. In this paper, we describe our approach to designing the course materials along with student assessment and evaluation for the “Hardware Architectures for Machine Learning” course targeting Electrical and Computer Engineering graduate students.

Cite this Paper

BibTeX
@InProceedings{pmlr-v170-chin22a, title = {Putting the "Machine" Back in Machine Learning for Engineering Students}, author = {Chin, Rudy and Stamoulis, Dimitrios and Marculescu, Diana}, booktitle = {Proceedings of the Second Teaching Machine Learning and Artificial Intelligence Workshop}, pages = {78--82}, year = {2022}, editor = {Kinnaird, Katherine M. and Steinbach, Peter and Guhr, Oliver}, volume = {170}, series = {Proceedings of Machine Learning Research}, month = {08--13 Sep}, publisher = {PMLR}, pdf = {https://proceedings.mlr.press/v170/chin22a/chin22a.pdf}, url = {https://proceedings.mlr.press/v170/chin22a.html}, abstract = {Computer hardware architecture has played an important role in the recent advances made in deep learning and associated applications. However, effective teaching strategies for hardware architectures for machine learning require a different structure and technical background than classic machine learning. More specifically, not only does the material need to convey necessary machine learning concepts to students, but also covers the hardware and software infrastructure concepts required for supporting machine learning systems. In this paper, we describe our approach to designing the course materials along with student assessment and evaluation for the “Hardware Architectures for Machine Learning” course targeting Electrical and Computer Engineering graduate students.} }
Endnote
%0 Conference Paper %T Putting the "Machine" Back in Machine Learning for Engineering Students %A Rudy Chin %A Dimitrios Stamoulis %A Diana Marculescu %B Proceedings of the Second Teaching Machine Learning and Artificial Intelligence Workshop %C Proceedings of Machine Learning Research %D 2022 %E Katherine M. Kinnaird %E Peter Steinbach %E Oliver Guhr %F pmlr-v170-chin22a %I PMLR %P 78--82 %U https://proceedings.mlr.press/v170/chin22a.html %V 170 %X Computer hardware architecture has played an important role in the recent advances made in deep learning and associated applications. However, effective teaching strategies for hardware architectures for machine learning require a different structure and technical background than classic machine learning. More specifically, not only does the material need to convey necessary machine learning concepts to students, but also covers the hardware and software infrastructure concepts required for supporting machine learning systems. In this paper, we describe our approach to designing the course materials along with student assessment and evaluation for the “Hardware Architectures for Machine Learning” course targeting Electrical and Computer Engineering graduate students.
APA
Chin, R., Stamoulis, D. & Marculescu, D.. (2022). Putting the "Machine" Back in Machine Learning for Engineering Students. Proceedings of the Second Teaching Machine Learning and Artificial Intelligence Workshop, in Proceedings of Machine Learning Research 170:78-82 Available from https://proceedings.mlr.press/v170/chin22a.html.

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