Machine learning is a powerful tool to find explanations for data, but not all explanations are created equal. This talk will explore why collaboration with domain experts is critical for the successful application of machine learning in the industrial and scientific domains.
In this talk we will focus on global optimisation of black-box functions using Bayesian optimisation, an iterative optimisation technique.
Regression models can be useful in various ways. In this talk we will focus on how we can use them to compute causal effects, so we can augment our toolkit when reasoning about how the world around us works. We will review some basic concepts of causal reasoning and revisit some common statistical misconceptions that can easily avoided with proper causal thinking.
Inverse problems arise when we want to use data to extract an insight into the inner workings of a system. Such problems are often ill-posed, meaning that there are multiple explanations compatible with the observations and it is therefore necessary to constrain the problem to arrive at a concrete solution.