The goal of classical geodetic data analysis is often to estimate distributional parameters like expected values and variances based on measurements that are subject to uncertainty due to unpredictable environmental effects and instrument specific noise. Its traditional roots and focus on analytical solutions at times require strong prior assumptions regarding problem specification and underlying probability distributions that preclude successful application in practical cases for which the goal is not regression in presence of Gaussian noise.

Machine learning methods are more flexible with respect to assumed regularity of the input and the form of the desired outputs and allow for nonparametric stochastic models at the cost of substituting easily analyzable closed form solutions by numerical schemes. This article aims at examining common grounds of geodetic data analysis and machine learning and showcases applications of algorithms for supervised and unsupervised learning to tasks concerned with optimal estimation, signal separation, danger assessment and design of measurement strategies that occur frequently and naturally in geodesy.