Technology Fundamentals: Supervised Versus Unsupervised Machine Learning

In the field of Machine Learning, there are two1 predominant strategies to train a machine learning algorithm: supervised learning and unsupervised learning. Common to both strategies is the idea of an example (e.g., an email, an image, a video snippet, etc.) and the translation of these examples into features (e.g., the sender, receiver, and subject of an email). Exactly how this translation works is outside the scope of this article and I leave it as an exercise for the reader. From a high-level perspective, in supervised machine learning, the algorithm is provided with a set of examples such that each example has a label (e.g., whether an email is spam) and the algorithm attempts to "figure out"2 how to map these examples to their corresponding labels. Conversely, in unsupervised learning, an algorithm is given only the examples and attempts to find patterns and similarities between the provided examples.