Teaching Key Machine Learning Principles Using Anti-learning Datasets
This work addresses educational challenges for machine learning students by introducing incremental teaching methods to enhance understanding of core principles.
The paper tackles the problem of teaching machine learning by advocating for alternative generalization methods, including anti-learning, to help students understand the importance of validation and problem-specific approaches, showing that different cross-validation granularities yield varying results.
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.