  X-ray reverberation has become a powerful tool to probe the disc-corona
geometry near black holes. Here, we develop Machine Learning (ML) models to
extract the X-ray reverberation features imprinted in the Power Spectral
Density (PSD) of AGN. The machine is trained using simulated PSDs in the form
of a simple power-law encoded with the relativistic echo features. Dictionary
Learning and sparse coding algorithms are used for the PSD reconstruction, by
transforming the noisy PSD to a representative sparse version. Then, the
Support Vector Machine is employed to extract the interpretable reverberation
features from the reconstructed PSD that holds the information of the source
height. The results show that the accuracy of predicting the source height,
$h$, is genuinely high and the misclassification is only found when $h$ >
15$r_g$. When the test PSD has a bending power-law shape, which is completely
new to the machine, the accuracy is still high. Therefore, the ML model does
not require the intrinsic shape of the PSD to be determined in advance. By
focusing on the PSD parameter space observed in real AGN data, classification
for $h \leq$ 10$r_g$ can be determined with 100% accuracy, even using a PSD in
an energy band that contains a reflection flux as low as 10% of the total flux.
For $h$ > 10$r_g$, the data, if misclassified, will have small uncertainties of
$\Delta h$ ~ 2-4$r_g$. This work shows, as a proof of concept, that the ML
technique could shape new methodological directions in the X-ray reverberation
analysis.
