Exponential Families and Kernels
In this course I will discuss how exponential families, a standard tool
in statistics, can be used with great success in machine learning to
unify many existing algorithms and to invent novel ones quite
effortlessly. In particular, I will show how they can be used in feature
space to recover Gaussian Process classification for multiclass
discrimination, sequence annotation (via Conditional Random Fields), and
how they can lead to Gaussian Process Regression with heteroscedastic
Elementary Linear Algebra
Experience with Bayesian Methods is beneficial, however not required.
Experience with Kernel Methods is likewise beneficial, but not
A background in statistics would be useful, however it is not required.
Conditioning and Feature Spaces
Applications 1 (Classification, Regression, Novelty Detection)
Applications 2 (Graphical Models and Conditional Random Fields)