Abstract: Machine learning methods are increasingly deployed in interactive environments, ranging from dynamic treatment strategies in medicine to fine-tuning of LLMs using reinforcement learning. In these settings, the learning agent interacts with the environment to collect data and necessarily faces an exploration-exploitation dilemma.In these lectures, we’ll begin with multi-armed bandits, progressing through structured and contextual bandits. We’ll then move on to reinforcement learning and broader decision-making frameworks, outlining the key algorithmic approaches and statistical principles that underpin each setting. Our goal is to develop both a rigorous understanding of the learning guarantees and a toolbox of fundamental algorithms.