### Lectures

header.tex

Introduction [ps]
[pdf]

Maximal margin classification [ps]
[pdf]

Introduction to kernels [ps]
[pdf]

Ridge regression and kernels [ps]
[pdf]

Properties of kernels [ps]
[pdf]

Soft-margin SVM, sparseness [ps]
[pdf]

Regression, the SVD and PCA [ps]
[pdf]

Kernel PCA and kernel CCA

Incomplete Cholesky decomposition [ps]
[pdf]

ANOVA kernels and diffusion kernels [ps]
[pdf]

String kernels and marginalized kernels [ps]
[pdf]

Fisher kernels and semidefinite programming [ps]
[pdf]

Multiple kernels and RKHS introduction [ps]
[pdf]

Reproducing kernel Hilbert spaces I [ps]
[pdf]

Reproducing kernel Hilbert spaces II [ps]
[pdf]

The Representer Theorem [ps]
[pdf]

Gaussian processes I [ps]
[pdf]

Gaussian processes II [ps]
[pdf]

Gaussian processes and reproducing kernels [ps]
[pdf]

Spectral clustering [ps]
[pdf]

Spectral clustering, introduction to Bayesian methods [ps]
[pdf]

Conjugacy and exponential family [ps]
[pdf]

Importance sampling and MCMC

Properties of Dirichlet distribution [ps]
[pdf]

Dirichlet processes I [ps]
[pdf]

Dirichlet processes II [ps]
[pdf]

Dirichlet process mixtures I [ps]
[pdf]

Dirichlet process mixtures II [ps]
[pdf]

Probabilistic formulation of prediction problems
[ps]

Risk bounds, concentration inequalities
[ps]

Glivenko-Cantelli classes and Rademacher averages
[ps]

Growth function and VC-dimension
[ps]

Applications of Rademacher averages in large margin
classification
[ps]

Growth function estimates for parameterized binary classes
[ps]

Covering numbers and metric entropy
[ps]

Chaining, Dudley's entropy integral
[ps]

Covering numbers of VC classes
[ps]

Bernstein's inequality, and generalizations
[ps]