  In this series of papers, we employ several machine learning (ML) methods to
classify the point-like sources from the miniJPAS catalogue, and identify
quasar candidates. Since no representative sample of spectroscopically
confirmed sources exists at present to train these ML algorithms, we rely on
mock catalogues. In this first paper we develop a pipeline to compute synthetic
photometry of quasars, galaxies and stars using spectra of objects targeted as
quasars in the Sloan Digital Sky Survey. To match the same depths and
signal-to-noise ratio distributions in all bands expected for miniJPAS point
sources in the range $17.5\leq r<24$, we augment our sample of available
spectra by shifting the original $r$-band magnitude distributions towards the
faint end, ensure that the relative incidence rates of the different objects
are distributed according to their respective luminosity functions, and perform
a thorough modeling of the noise distribution in each filter, by sampling the
flux variance either from Gaussian realizations with given widths, or from
combinations of Gaussian functions. Finally, we also add in the mocks the
patterns of non-detections which are present in all real observations. Although
the mock catalogues presented in this work are a first step towards simulated
data sets that match the properties of the miniJPAS observations, these mocks
can be adapted to serve the purposes of other photometric surveys.
