How does Bayesian optimization help to find the proper hyperparameters for a machine learning model?

Bayesian optimization works by constructing a posterior distribution of the objective function (Gaussian process) and use it to select the most promising hyperparameters to evaluate.
As the number of observations grows, the posterior distribution improves, and the algorithm becomes more certain of which regions in the parameter space are worth exploring, and which are not.

Good blogposts to learn about Bayesian optimization: [at towardsdatascience] [at research.fb.com]