Machine Learning is becoming a pervasive and disruptive technology. Its algorithms are making their ways in our devices, appliances, transports, and changing the way we invest, buy, search, drive, record, write, etc. .

But what is Machine Learning ? In order to understand the world we always work with models. The essence of Science is to construct models or theories. Simple models can be understood as mathematical equations: Force = Mass x Acceleration. This allows the inference of one quantity from another. But many things one would like to understand are intrinsically complex, and there is no hope of such simple formulas or they may not be adequately understood. The technology of machine learning allows one nevertheless to build models (in a computer) that can be used for accurate prediction and reliable decision making. The way this works is conceptually the same – the computer needs to infer a mathematical relationship between the inputs (say the pixels or points of colour in a photograph) and the outputs, a categorisation of what those pixels represent. The difference with simple models is both of scale, the amounts of data, and of complexity, the structure of data and the facets of the model.

In the MLRG, our research ranges from core theory to wide ranges applications, with connections to numerous fields outside machine learning. Our research directions pertain to the composability and servicisation of machine learning, making the field secure, transparent and efficient, and improving its scalability. Even when we have a strong emphasis on text and spatio-temporal data, we are keen to put our hands on all kinds of data.