Statistical Learning Theory and Applications >> Content Detail



These are examples of problem sets and a project for the class.
Problem Sets

Problem Set 1: Kernel Hilbert Spaces  (PDF)
Problem Set 2: RBF Interpolation Schemes (PDF)

Project (PDF)

For the final project, students select from one of the following suggested topics, and solve the problem that is described. If students prefer, they can bring their own project ideas to the professor or TAs for approval.


Hypothesis testing with small sets
Connection between MED and regularization
Feature selection for SVMs theory and experiments
Bayes classification rule and SVMs
IOHMMs evaluation of HMMs for classification vs. direct classification
Reusing the test set datamining bounds
Large-scale nonlinear least square regularization
Viewbased classification
Local vs. global classifiers experiments and theory
RKHS invariance to measure historical math
Concentration experiments (dot product vs. square distance)
Decorrelating classifiers: experiments about generalization using a tree of stumps
Kernel synthesis and selection
Bayesian interpretation of regularization and in particular of SVMs
History of induction from Kant to Popper and current state
Bayesian Priorhood



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