This course will strive the balance the theoretical foundations with practical applications of algorithms for optimal control and state estimation. The theoretical foundation serves as an introduction of the formalisms leveraged in modern Reinforcement Learning (e.g., Dynamic Programing). The practical applications will cover fundamental algorithms in the fields of robotics, aerospace, and electro-mechanical systems. Some algorithms include: the Linear Quadratic Regulator (LQR), Model Predictive Control (MPC), and Extended Kalman Filter (EKF).