uncertainty
3 free lessons tagged uncertainty across AI, Business. 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.
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.
Explaining Uncertainty, Error, and What the Model Cannot Do
Technical work is probabilistic and stakeholders want guarantees. This lesson covers communicating error rates in terms people can act on, why the two kinds of mistake are not interchangeable, setting expectations before deployment rather than after, and saying no to a request that will not work.
Abstention: Building a System That Can Say It Does Not Know
The most valuable behaviour an AI system can have is refusing to answer when it should not. This lesson covers calibration and why stated confidence is unreliable, selective prediction and the coverage-accuracy trade, conformal methods that give guarantees, designing abstention users accept, and measuring all of it in production.