Model predictive control (MPC) has become the most popular advanced control method in use today. Its main attractive features are (i) optimization of a model forecast over the available actuators (ii) estimation of the state of the system and disturbances from the process measurements, (iii) accounting for the process and actuator constraints, and (iv) accounting for full multivariable interactions. After its introduction in the process industries in the 1970s, MPC has today become a pervasive control technology in many industries, and is now being increasingly deployed for optimization of high-level functions such as minimizing energy consumption and maximizing product quality. This two-day workshop is intended to introduce graduate students and practitioners to the theory and design of MPC systems. Simulation examples are implemented in a high-quality open source software environment (python, octave, casADI). Students are expected to bring their own laptop computers and to download and install the workshop courseware prior to the class. Topics covered include regulation, state estimation, disturbance models and offset-free control, nonlinear MPC, nonlinear moving horizon estimation, economic MPC, suboptimal MPC, and MPC with discrete actuators.