  For quantum systems with a total dimension greater than six, the positive
partial transposition (PPT) criterion is sufficient but not necessary to decide
the non-separability of quantum states. Here, we present an Automated Machine
Learning approach to classify random states of two qutrits as separable or
entangled using enough data to perform a quantum state tomography, without any
direct measurement of its entanglement. We could successfully apply our
framework even when the Peres-Horodecki criterion fails. In addition, we could
also estimate the Generalized Robustness of Entanglement with regression
techniques and use it to validate our classifiers.
