Singular Learning Theory

Singular Learning Theory (SLT) is a novel mathematical framework that expands and improves upon traditional Statistical Learning theory using techniques from algebraic geometry, bayesian statistics, and statistical physics. It has great promise for the mathematical foundations of modern machine learning. 

From the meta-uni seminar on SLT:

The canonical references are Watanabe’s two textbooks:

Some other introductory references: