ABSTRACT
Machine learning methods are sometimes called subsymbolic because no symbols or symbolic manipulation are involved. Machine learning, then, is about making computers modify or adapt their actions so that these actions get more accurate, where accuracy is measured by how well the chosen actions reflect the correct ones. This chapter argues that students have some problem that they are interested in using machine learning on, such as the coin classification. It examines the process by which machine learning algorithms can be selected, applied, and evaluated for the problem. The complexity is often broken into two parts: the complexity of training, and the complexity of applying the trained algorithm. Training does not happen very often, and is not usually time critical, so it can take longer. The chapter also presents an overview of the key concepts discussed in this book.
