Artificial Intelligence and machine learning models have grown in both sophistication and complexity, and have demonstrated impressive accuracy and allowed deeper insights into claims, morbidity, customer stratification, and consumer behavior, to name a few. At the same time new techniques are being continuously developed, with ensemble learning methods replacing a single optimized model and new applications using Bayesian inference. To take advantage of this revolution in data, actuaries will need to understand both the theoretical underpinnings and practical applications of these models. We will briefly survey the landscape of models and explain how to construct and evaluate them, with an eye towards specific applications like group morbidity projection. We will also demonstrate concrete examples for how and why some fundamental models can be used to model uncertain quantities in health insurance in ways that improve on traditional actuarial predictions. Finally we will look at the frontier of advanced analytics and survey cutting edge techniques such as Bayesian Additive Regression Trees that may be implemented in the future to improve actuarial practice. Understand theoretical motivation and concrete application of latest machine learning models, and how and when to apply them.