The goal of scattered data interpolation techniques is to construct a (typically smooth) function from a set of unorganized samples. These techniques have a wide range of applications in computer graphics and computer vision. For instance they can be used to model a surface from a set of sparse samples, to reconstruct a BRDF from a set of measurements, or to interpolate motion capture data. This course will survey and compare scattered interpolation algorithms and describe their applications in computer graphics. Although the course is focused on applying these techniques, we will introduce some of the underlying mathematical theory and briefly mention numerical considerations.