Machine Learning in Cosmology

The evolution of the likelihood in the Bayes theorem is a computationally expensive task and strongly depends on the data used to constrain the model parameters. Finding a way to overcome this issue seems to be very attractive. It seems that certain types of Machine Learning tools can be very useful in such tasks. In particular, Bayesian ML is among them. In this talk, I will discuss what we have learned about the H0 tension problem using Bayesian ML by generating data from the considered models and what are the new hints inferred from considered scenarios.