Supervised learning approaches that do not explicitly take the time component into account are briefly discussed in this chapter. The approaches explained include feedforward neural networks, support vector machines, k-nearest neighbor, decision trees, naïve bayes and ensembles. Guidelines are provided on how to apply these algorithms to quantified self data, including the learning setup (e.g. learning for single users or across multiple users) and other practical considerations such as feature selection and regularization. Data stream mining approaches for predictive modeling are also briefly discussed.