This lecture focuses on deterministic interpolation methods, which are divided into global and local approaches. Global methods consider all measurement points in a domain, while local methods only use a limited number of support points. Local methods, such as nearest neighbor and inverse distance weighting, are preferred due to their ability to provide more realistic approximations. However, these methods can be subjective and lead to arbitrary results, introducing significant uncertainty.