Bayesian Optimization: Tuning Expensive Black-Box Functions

Some functions cost hours or dollars to evaluate once and give no gradient, so you must find their optimum in as few tries as possible. This path builds the standard method for that: why grid and random search waste the budget, how a Gaussian process predicts the objective and its uncertainty everywhere, how acquisition functions like Expected Improvement and the no-regret GP-UCB of Srinivas, Krause, Kakade, and Seeger decide where to look next, and how the tools work in practice. Grounded in the landmark papers of the field.