9.520 | Spring 2006 | Graduate

Statistical Learning Theory and Applications


Problem Sets

Problem Set 1: Kernel Hilbert Spaces  (PDF)

Problem Set 2: RBF Interpolation Schemes (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.

Project list and descriptions (PDF)


  • 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

Course Info

As Taught In
Spring 2006