Teaching Key Machine Learning Principles Using Anti-learning Datasets

TL;DR

This paper advocates teaching alternative machine learning methods like anti-learning to deepen understanding of validation and data requirements, highlighting the importance of diverse approaches and proper validation techniques.

Contribution

It introduces anti-learning as an alternative teaching method and emphasizes the importance of validation and data sufficiency in machine learning education.

Findings

Anti-learning helps students understand model generalization.

Different cross-validation granularities significantly affect results.

Proper validation is crucial for reliable machine learning models.

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.