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Teaching » Syllabus: Current Topics in Statistical Learning

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Date Topic Reading Assignments
Jan. 12 Introduction TBA
Jan. 26 Bayesian and Approximate Bayesian Learning: Gaussian Approximations, Variational Bayes, and Markov Monte-Carlo Chain Methods MacKay Ch.27, Bishop Ch.10, p397

Variational Approximation: Paper 1 (especially introduction),Paper 2, MacKay Ch.33
Markov Chain Monte Carlo Methods: MacKay Ch.29&30, Paper 3

Feb. 2 Introduction to Gaussian Processes

Tutorial MacKay, Brief Article Williams, Tutorial Williams

Feb. 9 Research on Gaussian Processes

Sparsification, Matern Kernels, Derivative GPs, Warped GPs

Feb. 23 An Introduction to Dirichlet Processes

Aaron's Notes

March 1

Research on Dirichlet Processes

Neal-1998, Escobar-1995

March 8

More Dirichlet Processes

Rasmussen 2002, Blei 2004, Variational Dirichlet Processes,

Mar. 15

Spring Break TBA

Mar. 22

Dynamic Bayesian Networks and Sequential Estimation Techniques

Kalman filtering and smoothing (paper sent by email), Particle Filter Tutorial, Dynamic Bayesian Nets

Mar. 29

Dynamic Bayesian Networks and Sequential Estimation Techniques: Research Papers

Adaptive Classification with Kalman Filter, Particle Filters for localization in Robotics, Combined Parameter Estimation and State Estimation with Particle Filters, Information Filter for robot localization

Apr. 7 (!!!)

Linear and Nonlinear Dimensionality Reduction: PCA, Factor Analysis, Isomap, LLE

Factor Analysis, ISOMAP, LLE, More LLE

Apr. 12

Dimensionality Reduction: Current Research

RKHS-Dim.Reduction, Kernel-PCA, Kernel-PCA-notes, Bayesian-PCA

Apr. 19

Bootstrapping and Boosting TBA

Apr. 26

Project Presentations
Designed by: Nerses Ohanyan & Jan Peters
Page last modified on October 21, 2005, at 05:58 PM