| 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
|
|
