@inproceedings{2b0c8ec621524b82aa58feb6280bcef1,
title = "Teaching Key Machine Learning Principles Using Anti-learning Datasets",
abstract = "Much of the teaching of machine learning focuses on iterative hill-climbing approaches and the use of local knowledge to gain information leading to local or global maxima. In this paper we advocate the teaching of alternative methods of generalising to the best possible solution, including a method called anti-learning. By using simple teaching methods, students can achieve a deeper understanding of the importance of validation on data excluded from the training process and that each problem requires its own methods to solve. We also exemplify the requirement to train a model using sufficient data by showing that different granularities of cross-validation can yield very different results.",
keywords = "anti-learning, exclusive-or, hadamard, machine learning",
author = "Chris Roadknight and Prapa Rattadilok and Uwe Aickelin",
note = "{\textcopyright} 2018 IEEE.; 2018 IEEE International Conference on Teaching, Assessment, and Learning for Engineering, TALE 2018 ; Conference date: 04-12-2018 Through 07-12-2018",
year = "2019",
month = jan,
day = "16",
doi = "10.1109/TALE.2018.8615252",
language = "English",
series = "Proceedings of 2018 IEEE International Conference on Teaching, Assessment, and Learning for Engineering, TALE 2018",
publisher = "Institute of Electrical and Electronics Engineers (IEEE)",
pages = "960--964",
editor = "Lee, {Mark J.W.} and Sasha Nikolic and Wong, {Gary K.W.} and Jun Shen and Montserrat Ros and Lei, {Leon C. U.} and Neelakantam Venkatarayalu",
booktitle = "Proceedings of 2018 IEEE International Conference on Teaching, Assessment, and Learning for Engineering, TALE 2018",
address = "United States",
}