Week 4: Basis Functions

For this week the lecture slides are available here

YouTube Videos

There is a YouTube video available of me giving this material at the Gaussian Process Road Show in Uganda. The first 25 minutes leads covers model selection, and then it leads into the video second below which introduces Bayesian modelling.

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/week4.ipynb', 'week4.ipynb')

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

Reading

• Sections 1.5-1.6 of Rogers and Girolami.
• Sections 3.1-3.4 (pg 95-117) Although you haven't covered the beta density yet.
• Section 1.2.3 (pg 21-24) and 1.2.6 (start from just past eq 1.64 pg 30-32) of Bishop
• Section 1.3 of Bishop (pg 32-33)

Learning Outcomes Week 4

• Understand that basis functions allow for non-linear regression.
• Understand the difference between non-linear in the parameters and non-linear in the inputs.
• Understand the difference betwen local basis functions, like RBF and golbal like polynomials or the Fourier basis.
• Find the stationary point of a sum of squares objective function where the prediction function is a linear combination of a basis set.

This document last modified Tuesday, 21-Oct-2014 09:56:15 UTC