The bootcamp will provide the background that is essential for the different communities to better engage in the program. In particular, we will start with one day on Bayesian methods with known priors (TCS: prophets, ML: MDPs and Bayesian bandits, EconCS/OR: Dynamic mechanism design) or unknown priors (TCS: random-arrival models, ML: stochastic bandits, reinforcement learning). We will continue with one day on adversarial settings and maximin guarantees (TCS: competitive analysis, ML: online learning, and connections via mirror descent). Subsequently, we will discuss the main concepts of the semester that include hybrid decision-processes (settings between stochastic and adversarial, incentive-aware learning), understanding the effect of different constraint structures in sequential decision-making and discussing societal considerations in data-driven decision processes. The final day of the bootcamp will be devoted to celebrating the legacy of David Blackwell and his seminal contributions to the field.