the gentlest possible introduction to generalization error bounds beyond uniform convergence

just some basic statistical learning *practice*

generalizing linear discriminant analysis beyond normally distributed data

bayesian inference in model misspecification settings, visually explained

a toy bayesian neural network with an exact $ \beta \mid D $

never escaping jekyll :)