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Online Learning is a relatively old branch of machine learning that has recently regained favour for two reasons. Firstly, online learning algorithms such as Stochastic Gradient Descent work extremely well on very large data sets which have become increasingly prevalent (and increasingly large!). Secondly, there has been a lot of important theoretical steps made recently in understand the convergence behaviour of these algorithms and their relationship to traditional Empirical Risk Minimisation (ERM) algorithms such as Support Vector Machines (SVMs). In order to understand these algorithms better, I implemented a recent one (Pegasos, described below) in Clojure. This had the added advantage of seeing how well Clojure’s performance held up when doing some serious number-crunching.