Course Description
Theoretical foundations of generalization. PAC learning framework: probably approximately correct guarantees. VC dimension: definition, examples, fundamental theorem of learning. Rademacher complexity: data-dependent generalization bounds. PAC-Bayes bounds and their connection to Bayesian methods. Double descent and the interpolation threshold. Overparameterization and implicit regularization in neural networks. Connections to information theory: MDL principle.