Bayesian Review and Gaussian Processes
Gaussian Processes Lecture Slides.
There is a YouTube video available of me giving this material at the Gaussian Process Road Show in Uganda.
GPRS Uganda Video
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://github.com/SheffieldML/notebook/blob/master/lab_classes/machine_learning/MLAI_lab5.ipynb', 'MLAI_lab5.ipynb')
You should now be able to find the lab class by clicking
the ipython notebook menu.
- 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 7
- Understanding how the marginal likelihood in a Gaussian Bayesian regression model can be computed using properties of the multivariate Gaussian.
- Understanding that Bayesian Regression Models put a joint Gaussian prior across the data.
- Understanding that we can specify the covariance function of that prior directly.
- Understanding that Gaussian process models generalize basis function models to allow infinite basis functions.