  This paper presents an overview of some of the concepts of Bayesian Learning.
The number of scientific and industrial applications of Bayesian learning has
been growing in size rapidly over the last few decades. This process has
started with the wide use of Markov Chain Monte Carlo methods that emerged as a
dominant computational technique for Bayesian in the early 1990's. Since then
Bayesian learning has spread well across several fields from robotics and
machine learning to medical applications. This paper provides an overview of
some of the widely used concepts and shows several applications. This is a
paper based on the series of seminars given by students of a PhD course on
Bayesian Learning at George Mason University. The course was taught in the Fall
of 2021. Thus, the topics covered in the paper reflect the topics students
selected to study.
