In quantitative MRI, tissue properties are typically estimated by fitting a signal model onto the acquired data. These models are derived from the underlying MR physics describing the signal behavior. The accuracy of the quantitative values heavily depends on the correctness of this model which is usually validated using gold-standard sequences with long acquisition times. Here, we suggest learning the signal model based on the values obtained from the gold-standard sequence with machine learning methods instead. The feasibility of the idea is shown using quantitative T2-mapping with a multi-echo spin-echo sequence and a classical single spin-echo as the gold standard.