In this project, a unique method of combining online learning with model predictive control is applied to autonomous racing. A concern in autonomous racing is that accurate models that encapsulate the dynamics of the vehicle are complex, nonlinear, and difficult to identify. In order to make this more practical for control purposes, the controller is initialized with a nominal tire model, which then learns tire properties online using locally weighted projection regression during the course of the race. This makes it more practical for control purposes while maintaining model accuracy. Focus is placed on learning the tire properties which in reality, keep varying due to wear, temperature and pressure fluctuations, etc. The main objective is to minimize lap times by allowing the controller to "learn" its varying tire behavior while on the track.