A Deep Learning Bootcamp for Engineering & Management Students

Lukas Lodes, Alexander Schiendorfer
Proceedings of the Third Teaching Machine Learning and Artificial Intelligence Workshop, PMLR 207:32-36, 2023.

Abstract

Students from engineering & management with a focus on vocational activities such as machining or accounting tend to lack the necessary computer science foundations to build, appreciate, and evaluate machine learning solutions. However, they are likely going to have to identify and judge potential use cases in their careers in industrial practice. Therefore, we propose a guided three-day curriculum that goes all the way from manual data inspection to the implementation of several models in Python, including several evaluation metrics. We focus on a computer vision task identifying traffic signs due to its conceptual simplicity and similarity to tasks in vision-based quality assurance. In this paper, we share our material as OER as well as experiences we’ve made throughout three offerings of the bootcamp.

Cite this Paper

BibTeX
@InProceedings{pmlr-v207-lodes23a, title = {A Deep Learning Bootcamp for Engineering & Management Students}, author = {Lodes, Lukas and Schiendorfer, Alexander}, booktitle = {Proceedings of the Third Teaching Machine Learning and Artificial Intelligence Workshop}, pages = {32--36}, year = {2023}, editor = {Kinnaird, Katherine M. and Steinbach, Peter and Guhr, Oliver}, volume = {207}, series = {Proceedings of Machine Learning Research}, month = {19--23 Sep}, publisher = {PMLR}, pdf = {https://proceedings.mlr.press/v207/lodes23a/lodes23a.pdf}, url = {https://proceedings.mlr.press/v207/lodes23a.html}, abstract = {Students from engineering & management with a focus on vocational activities such as machining or accounting tend to lack the necessary computer science foundations to build, appreciate, and evaluate machine learning solutions. However, they are likely going to have to identify and judge potential use cases in their careers in industrial practice. Therefore, we propose a guided three-day curriculum that goes all the way from manual data inspection to the implementation of several models in Python, including several evaluation metrics. We focus on a computer vision task identifying traffic signs due to its conceptual simplicity and similarity to tasks in vision-based quality assurance. In this paper, we share our material as OER as well as experiences we’ve made throughout three offerings of the bootcamp.} }
Endnote
%0 Conference Paper %T A Deep Learning Bootcamp for Engineering & Management Students %A Lukas Lodes %A Alexander Schiendorfer %B Proceedings of the Third Teaching Machine Learning and Artificial Intelligence Workshop %C Proceedings of Machine Learning Research %D 2023 %E Katherine M. Kinnaird %E Peter Steinbach %E Oliver Guhr %F pmlr-v207-lodes23a %I PMLR %P 32--36 %U https://proceedings.mlr.press/v207/lodes23a.html %V 207 %X Students from engineering & management with a focus on vocational activities such as machining or accounting tend to lack the necessary computer science foundations to build, appreciate, and evaluate machine learning solutions. However, they are likely going to have to identify and judge potential use cases in their careers in industrial practice. Therefore, we propose a guided three-day curriculum that goes all the way from manual data inspection to the implementation of several models in Python, including several evaluation metrics. We focus on a computer vision task identifying traffic signs due to its conceptual simplicity and similarity to tasks in vision-based quality assurance. In this paper, we share our material as OER as well as experiences we’ve made throughout three offerings of the bootcamp.
APA
Lodes, L. & Schiendorfer, A.. (2023). A Deep Learning Bootcamp for Engineering & Management Students. Proceedings of the Third Teaching Machine Learning and Artificial Intelligence Workshop, in Proceedings of Machine Learning Research 207:32-36 Available from https://proceedings.mlr.press/v207/lodes23a.html.

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