  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.
