Open the slides · Read the worked handout · Read the primer.
The lecture connects a noisy scientific measurement to a forward model, probability, and uncertainty. It introduces dependence and the central limit theorem, then develops vectors, features, transformations, and projection as ways to describe and compare data. Dimensionality reduction provides a preview of the later PCA discussion.
The worked handout follows the lecture and retains the derivations and examples. Return to the Lecture Library.