Will the sun shine? - An accessible dataset for teaching machine learning and deep learning

Florian Huber, Erica Dafne van Kuppevelt, Peter Steinbach, Colin Sauze, Yang Liu, Berend Weel
Proceedings of the Third Teaching Machine Learning and Artificial Intelligence Workshop, PMLR 207:27-31, 2023.

Abstract

Hands-on teaching of modern machine learning and deep learning techniques heavily relies on the use of well-suited datasets. We present a novel tabular dataset that was specifically created for teaching machine learning and deep learning to an academic audience. The dataset contains intuitively accessible weather observations from 18 locations in Europe. It was designed to be suitable for a large variety of different training goals, and to avoid reaching unrealistically high prediction accuracy. Teachers or instructors thus can choose the difficulty of the training goals and thereby match it with the respective learner audience or lesson objective.

Cite this Paper

BibTeX
@InProceedings{pmlr-v207-huber23a, title = {Will the sun shine? - An accessible dataset for teaching machine learning and deep learning}, author = {Huber, Florian and van Kuppevelt, Erica Dafne and Steinbach, Peter and Sauze, Colin and Liu, Yang and Weel, Berend}, booktitle = {Proceedings of the Third Teaching Machine Learning and Artificial Intelligence Workshop}, pages = {27--31}, 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/huber23a/huber23a.pdf}, url = {https://proceedings.mlr.press/v207/huber23a.html}, abstract = {Hands-on teaching of modern machine learning and deep learning techniques heavily relies on the use of well-suited datasets. We present a novel tabular dataset that was specifically created for teaching machine learning and deep learning to an academic audience. The dataset contains intuitively accessible weather observations from 18 locations in Europe. It was designed to be suitable for a large variety of different training goals, and to avoid reaching unrealistically high prediction accuracy. Teachers or instructors thus can choose the difficulty of the training goals and thereby match it with the respective learner audience or lesson objective.} }
Endnote
%0 Conference Paper %T Will the sun shine? - An accessible dataset for teaching machine learning and deep learning %A Florian Huber %A Erica Dafne van Kuppevelt %A Peter Steinbach %A Colin Sauze %A Yang Liu %A Berend Weel %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-huber23a %I PMLR %P 27--31 %U https://proceedings.mlr.press/v207/huber23a.html %V 207 %X Hands-on teaching of modern machine learning and deep learning techniques heavily relies on the use of well-suited datasets. We present a novel tabular dataset that was specifically created for teaching machine learning and deep learning to an academic audience. The dataset contains intuitively accessible weather observations from 18 locations in Europe. It was designed to be suitable for a large variety of different training goals, and to avoid reaching unrealistically high prediction accuracy. Teachers or instructors thus can choose the difficulty of the training goals and thereby match it with the respective learner audience or lesson objective.
APA
Huber, F., van Kuppevelt, E.D., Steinbach, P., Sauze, C., Liu, Y. & Weel, B.. (2023). Will the sun shine? - An accessible dataset for teaching machine learning and deep learning. Proceedings of the Third Teaching Machine Learning and Artificial Intelligence Workshop, in Proceedings of Machine Learning Research 207:27-31 Available from https://proceedings.mlr.press/v207/huber23a.html.

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