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The inverse analysis of indentation curves, aimed at extracting the stress-strain curve of a material, has been under intense development for decades, with progress relying mainly on the use of analytical expressions derived from small data sets. Here, we take a fresh, data-driven perspective to this classic problem, leveraging machine learning techniques to advance indentation technology. Using a neural network (NN), we efficiently assess uniqueness and identify materials that have indistinguishable indentation responses without the need for complex, domain knowledge-based algorithms. We then demonstrate that inclusion of the residual imprint information resolves the non-uniqueness problem. We show that the elasto-plastic properties of a material can be learned directly from indentation pile-up. Notably, an accurate stress-strain curve can be derived using solely the applied indentation load and pile-up …