Kernel methods give a systematic and principled approach to training
learning machines and the good generalization performance achieved
can be readily justified using statistical learning theory or Bayesian arguments. We describe how to use kernel methods for classification, regression and novelty detection and in each case we find that training can
be reduced to optimization of a convex cost function.
If you've ever asked yourself: "How do I choose the covariance function for a Gaussian process?" this is the page for you. Here you'll find concrete advice on how to choose a covariance function for your problem, or better yet, make your own.
This tutorial aims to provide an intuitive understanding of the Gaussian processes regression. Gaussian processes regression (GPR) models have been widely used in machine learning applications because of their representation flexibility and inherent uncertainty measures over predictions.
Broadly speaking, Machine Learning refers to the automated identification of patterns in data. As such it has been a fertile ground for new statistical and algorithmic developments. The purpose of this course is to provide a mathematically rigorous introduction to these developments with emphasis on methods and their analysis.