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Much of the teaching of machine learning focuses on iterative hill-climbingapproaches and the use of local knowledge to gain information leading to localor global maxima. In this paper we advocate the teaching of alternative methodsof generalising to the best possible solution, including a method calledanti-learning. By using simple teaching methods, students can achieve a deeperunderstanding of the importance of validation on data excluded from thetraining process and that each problem requires its own methods to solve. Wealso exemplify the requirement to train a model using sufficient data byshowing that different granularities of cross-validation can yield verydifferent results.

Teaching Key Machine Learning Principles Using Anti-learning Datasets