This subdirectory contains lab classes from my Masters course on machine learning. The material is targeted at students who have familiarity with programming, linear algrebra and probability. At the end of this course you have enough background material to begin the notebooks from the Gaussian process summer school.
Introduction to IPython notebooks and a refresher course on expectations and sampling from probability densities.
Linear regression in python with numpy.
Generalization: training sets and test sets, validation.
Bayesian approaches.
Gaussian process models.
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