Introduction
What this guide covers
Introduction Introduction Artificial Intelligence and Statistical Learning at Doctoral Level
Overview
Topics covered
- Introduction
- Statistical Learning Theory and Complexity
- Rademacher complexity
- Vapnik-Chervonenkis (VC) dimension
- Regularization and Optimization in Functional Spaces
- Regularization vs : Ridge and Lasso
Related concepts
Main connections
Compatible notation
Mathematical sample in LaTeX
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R(h) = \E_{(X,Y) \sim \mathbb{P}}[\ell(h(X), Y)] = \int_{\X \times \Y} \ell(h(x), y) \dd \mathbb{P}(x, y).