AI engineering glossary · Calculus optimisation
What is bayesian optimisation?
Bayesian optimisation: A strategy for optimising expensive black-box functions by fitting a probabilistic surrogate model, such as a Gaussian process, to the evaluations so far, then using an acquisition function to choose the next point that balances exploration and exploitation.
Bayesian optimisation: an example
To tune 5 hyperparameters of a model that takes 2 hours per training run, it picks each new configuration from a surrogate rather than at random.
Related terms
More Calculus optimisation terms
- BFGS
- Chain rule
- Computational graph
- Condition number
- Conjugate gradient method
- Constrained optimisation
- Constraint
- Continuity
Where you learn it
Bayesian optimisation is part of the AI Engineering Program, which teaches it with notes, a hands-on lab and an exam. The first lesson is free.