A special point in materials science is that data is typically not big. This implies that some ML methods are not suitable. In general, standard ML methods need to be used with caution and modification or new concepts have been and still need to be developed. Interestingly, Gaussian Process Regression and Random Forests are still often and helpfully used, but several new concepts were established in recent years, e.g., crystal-graph neural network, message passing and equivariance, subgroup discovery, and SISSO (sure independence screen and sparsifying operator). In particular the latter can deal with correlations between a big (even immense) number of elemental materials features (millions or trillions) and just a few data dozens data points of the property of interest. SISSO derives an analytical equation for describing the materials property and its statistical correlation with the relevant materials genes. The approach as well as recent advancements, implementations, and challenges are described by Yao et al. in contribution .