Machine learning algorithms come in many diferent flavors. Similar to humans, the machine itself learns in diferent ways.8 One of the most common ways in which algorithms learn is called supervised learning. Essentially an inductive approach to learning, algorithms are given a training set comprising the characteristics that engineers want the algorithm to detect and compare with new data (Flach, 2012). Importantly, the training set includes data about the desired output. When the training data do not include data about desired outputs, the approach is called unsupervised learning. Often, machine learning algorithms may fall somewhere in between: The data only contain a few desired outputs, which is also called semi-supervised learning (Domingos, 2015).9 Before an algorithm can be applied to learn from data, models have to be constructed that formalize the task and goals, so that it can be processed by a computer. For instance, before an algorithm can perform the task of finding the most important news feed stories, models have to be created to represent the relationship between news and relevance. As Mackenzie puts it:
The techniques of machine learning nearly all pivot around ways of transforming, constructing or imposing some kind of shape on the data and using that shape to discover, decide, classify, rank, cluster, recommend, label or predict what is happening or what will happen. (2015: 432)
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