Prerequisites: Basic familiarity with probability, fluency in a high-level programming language.

Autonomy for air and space vehicles is becoming an increasingly important field of study for aerospace researchers. Decision Making Under Uncertainty provides the mathematical and computational foundations to pursue research in the fields of decision-making and reinforcement learning. Specifically, this course covers topics including Markov Decision Processes (MDPs), Partially Observable MDPs, and their corresponding solvers (exact and approximate), as well as fundamentals of traditional and deepReinforcement Learning including model-free and model-based RL, Q-learning, policy gradients, actor-critics, etc. Students should have some basic familiarity with probability, fluency in a high-level programming language, and willingness to learn Python or Julia.