bayesian-optimization
4 free lessons tagged bayesian-optimization across AI. Each one is a short sequence of focused steps with narration and a five-question quiz at the end — take them in any order, no signup required.
Bayesian Optimization in Practice
The theory becomes useful through libraries, and its flagship job is tuning machine-learning models. This lesson covers that application, the Tree-structured Parzen Estimator behind Hyperopt and Optuna, the tools you would actually reach for, batching, and the honest limits of the method.
Acquisition Functions: Deciding Where to Look Next
A Gaussian process gives a mean and an uncertainty everywhere; the acquisition function turns them into one decision about where to spend the next expensive evaluation. This lesson covers Expected Improvement, GP-UCB and its no-regret guarantee, and Thompson sampling, and how each resolves exploration against exploitation.
The Gaussian Process Surrogate
Bayesian optimization needs a model that predicts the objective everywhere and knows how unsure it is. The Gaussian process delivers exactly that. This lesson builds the GP as a distribution over functions, shows how a kernel encodes assumptions, and explains why its posterior variance is the engine of the whole method.
Optimizing an Expensive Black Box
Some functions cost hours or dollars to evaluate once, give no gradient, and must be optimized in as few tries as possible. This lesson sets up that problem, shows why grid and random search waste the budget, and introduces the surrogate-model loop that Bayesian optimization is built on.