Teaching Key Machine Learning Principles Using Anti-learning Datasets

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...

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Bibliographic Details
Published in2018 IEEE International Conference on Teaching, Assessment, and Learning for Engineering (TALE) pp. 960 - 964
Main Authors Roadknight, Chris, Rattadilok, Prapa, Aickelin, Uwe
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.12.2018
Subjects
anti-learning
Australia
Data models
exclusive-or
hadamard
Machine learning
Machine learning algorithms
Training
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Summary: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.
ISSN:2470-6698
DOI:10.1109/TALE.2018.8615252