This talk will cover a broad survey of methods for incorporating both group- and individual-level information in machine learning for intensive longitudinal data. The broad survey will be followed by a deep dive into mixed effects machine learning, cluster-mean centering as a preprocessing step, and results from an empirical study of momentary affect prediction with passive sensing data. The group may discuss further directions and unanswered questions, use cases for this sort of work, recommended frameworks and approaches for intensive longitudinal machine learning, and how to incorporate cluster-mean centering into machine learning preprocessing.