  Redshift estimation and the classification of gamma-ray AGNs represent
crucial challenges in the field of gamma-ray astronomy. Recent efforts have
been made to tackle these problems using traditional machine learning methods.
However, the simplicity of existing algorithms, combined with their basic
implementations, underscores an opportunity and a need for further advancement
in this area. Our approach begins by implementing a Bayesian model for redshift
estimation, which can account for uncertainty while providing predictions with
the desired confidence level. Subsequently, we address the classification
problem by leveraging intelligent initialization techniques and employing soft
voting. Additionally, we explore several potential self-supervised algorithms
in their conventional form. Lastly, in addition to generating predictions for
data with missing outputs, we ensure that the theoretical assertions put forth
by both algorithms mutually reinforce each other.
