AA203: Optimal and Learning-Based Control
Stanford Online course providing basic solution techniques for optimal control and dynamic optimization problems, as found in work with rockets, robotic arms, autonomous cars, option pricing, and macroeconomics.
The course provides basic solution techniques for optimal control and dynamic optimization problems, such as those found in work with rockets, robotic arms, autonomous cars, option pricing, and macroeconomics. Learners develop an understanding of the theoretic and implementation aspects of various techniques, including dynamic programming, calculus of variations, model predictive control, and robot motion planning.
This is a curated third-party resource. All credit and citation belongs to Marco Pavone and Stanford University.