Lectures
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Tree Models
Cross Validation, Regularization, and Information Criteria
TIC/AIC
Bayesian Model Selection
MDL Introduction and Source Coding
Minimum Description Length
More on Marginal Likelihood
Approximation of Marginal Likelihood
Reversible Jump MCMC and Introduction to Kernel Methods (version 1)
Reversible Jump MCMC and Introduction to Kernel Methods (version 2)
Introduction to Support Vector Machines
Lagrangian Duality
Optimal Margin Classifiers
Introduction to Kernels
Support Vector Machines---Non-Separable
Classification and Regression
Kernel Principal Component Analysis
Reproducing Kernel Hilbert Spaces
Reproducing Kernel Hilbert Spaces II
The Representer Theorem
Regularization and RKHS
Fourier Perspective on Regularization
Gaussian Processes I
Gaussian Processes II
Gaussian Processes and Reproducing Kernels
Background on Uniform Convergence Bounds
Statistical Learning Theory---Finite Case I
Statistical Learning Theory---Finite Case II
Statistical Learning Theory---Symmetrization Lemma
Annealed Entropy and Growth Function
Vapnik-Chervonenkis Dimension
Structural Risk Minimization
Boosting I
Boosting II