Learning and Inference with Gaussian Processes


at School of Computer Science, University of Manchester, U.K. on Jun 22, 2006 [pdf]
Neil D. Lawrence, University of Sheffield



Many application domains of machine learning can be reduced to inference about the values of a function. Gaussian processes are powerful, flexible, probabilistic models that enable us to efficiently perform inference about functions in the presence of uncertainty. In this talk I will introduce Gaussian processes and review a few standard applications of these models. I will then show how Gaussian processes can be used to solve important and diverse real-world problems, including inference of the concentration of transcription factors which regulate gene expression and creating probabilistic models of human motion for animation and tracking.