Overview of Bayesian Optimization (BO), an intelligent sequential search strategy for optimizing expensive black-box functions where the relationship between inputs and outputs is unknown.

It contrasts BO with less efficient methods like Grid Search and Random Search, highlighting BO's superior sample efficiency due to its ability to learn from past evaluations.

The explanation breaks down BO into its two core components: the surrogate model, typically a Gaussian Process, which probabilistically maps the unknown function and quantifies uncertainty, and the acquisition function, which guides the search by balancing exploration and exploitation.

Finally, it explores diverse applications of BO, from hyperparameter tuning in machine learning to accelerating scientific discovery in materials science and drug design, while also discussing practical challenges and available software tools, emphasizing BO's increasing importance in various fields.