Machine Learning and cosmographic reconstructions of quintessence and
  the Swampland conjectures
  We present model independent reconstructions of quintessence and the
Swampland conjectures (SC) using both Machine Learning (ML) and cosmography. In
particular, we demonstrate how the synergies between theoretical analyses and
ML can provide key insights on the nature of dark energy and modified gravity.
Using the Hubble parameter $H(z)$ data from the cosmic chronometers we find
that the ML and cosmography reconstructions of the SC are compatible with
observations at low redshifts. Finally, including the growth rate data
$f\sigma_8(z)$ we perform a model independent test of modified gravity
cosmologies through two phase diagrams, namely $H-f\sigma_8$ and
$\eta-f\sigma_8$, where the anisotropic stress parameter $\eta$ is obtained via
the $E_g$ statistics, which is related to gravitational lensing data. While the
first diagram is consistent within the errors with the $\Lambda$CDM model, the
second one has a $\sim 2\sigma$ deviation of the anisotropic stress from unity
at $z\sim 0.3$ and a $\sim 4\sigma$ deviation at $z\sim 0.9$, thus pointing
toward mild deviations from General Relativity, which could be further tested
with upcoming large-scale structure surveys.
