Introduction to Bayesian ML/DL, with Application to Parameter Inference of Coupled Non-linear ODEs – Part 1

In this talk, the speaker will present introductory materials about Bayesian Machine Learning.

Abstract
Gaussian process(GP) is a stochastic process such that the joint distribution of an arbitrary finite subset of the random variables is a multivariate normal. It plays a fundamental role in Bayesian machine learning as it can be interpreted as a prior over functions (Rasmussen and Williams, 2006), hence providing a nonparametric approach to various tasks. In the first part, I will introduce the general framework of GP and some underlying theory, accompanied by an illustrative example of GP regression, also known as Kringing. In the second part, I will introduce some recent works on applying GP to parameter inference of coupled non-linear ODEs arising in various biological contexts.