Introduction
What this guide covers
An analytical treatise on statistical learning theory, Hilbert spaces, generative models, high-dimensional non-convex optimization, and equivariant dynamical systems.
Overview
Topics covered
- Introduction
- Advanced Topics in Artificial Intelligence
- VC Theory, Structural Complexity and Limits of Generalization
- Rademacher complexity
- Theory of Reproducible Kernels and Hilbert Spaces (RKHS)
- Reproduction Property in Hilbert Spaces (RKHS)
Related concepts
Main connections
Compatible notation
Mathematical sample in LaTeX
The expression is rendered accessibly, with its LaTeX source preserved below.
View LaTeX source
R(f) = \mathbb{E}_{(\mathbf{x}, y) \sim P} [\mathbb{I}(f(\mathbf{x}) \neq y)]