BYU CS classes

To understand Gaussian process regression, and to be able to generate nonparametric regressions with confidence intervals. Also to understand the interplay between a kernel (or covariance) function, and the resulting confidence intervals of the regression.

You will turn in an iPython notebook that performs Gaussian process regression on a simple dataset. You will explore multiple kernels and vary their parameter settings.

When you are done, you should produce visualizations like the following (for noiseless observations):

and like this (for noisy observations):

data_xvals = numpy.atleast_2d( [ 1.0, 3.0, 5.0, 6.0, 7.0, 8.0 ] )
data_yvals = numpy.sin( data_xvals )

The following functions may be useful to you:

plt.gca().fill_between
plt.scatter
 
numpy.linalg.inv
 
numpy.eye