Week 8: Dimensionality Reduction

Bayesian Inference Lecture Slides

Lab Class

The notebook for the lab class can be downloaded from here.

To obtain the lab class in ipython notebook, first open the ipython notebook. Then paste the following code into the ipython notebook

import urllib
urllib.urlretrieve('https://raw.githubusercontent.com/SheffieldML/notebook/master/lab_classes/machine_learning/week8.ipynb', 'week8.ipynb')

You should now be able to find the lab class by clicking File->Open on the ipython notebook menu.

Reading

• Rogers and Girolami Chapter 7: Up to page 249.

Learning Outcomes Week 8

• Understand the principles of latent variable modelling.
• Understand the origin of PCA and its relationship to factor analysis.
• Understand the eigenvalue problem for positive definite symmetric matrices and how it relates to the covariance matrix.
• Be able to derive the posterior density over the latent variables for probabilistic PCA and factor analysis.
• Be able to intelligently apply principal component analysis to mutlivariate data sets.
• Understand the separation between model and algorithm.

This document last modified Tuesday, 18-Nov-2014 08:36:20 UTC