Week 10: Bayesian Review and Gaussian Processes

Gaussian Processes Lecture Slides.

YouTube Videos

There is a YouTube video available of me giving this material at the Gaussian Process Road Show in Uganda.

GPRS Uganda Video

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/week12.ipynb', 'week12.ipynb')

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

Reading

• Section 3.7--3.8 of Rogers and Girolami (pg 122--133).
• Section 3.4 of Bishop (pg 161--165).
• Chapters 1 and 2 of Gaussian Processes for Machine Learning by Rasmussen and Williams

Learning Outcomes Week 10

• Understand the difference between a generative and discriminative model in classification
• Understanding that generative models such as naive Bayes can more easily handle missing data, but may require stronger assumptions than modeling directly the discriminative model.
• Understanding the use of a link function to allow a linear model to be combined with the Bernoulli distribution.
• Understand the form of the logit link function and the nature of the log odds.
• Understand the form of a generalised linear model such as logistic regression and how to derive gradients with respect to parameters to minimize the negative log likelihood.
• Be able to apply logistic regression to a classification data set.

This document last modified Tuesday, 16-Dec-2014 11:08:15 UTC