Statistics, Data science, Machine learning and AI

Location: Room 9.87, Worsley Building, University of Leeds

14:30-14:45: tea & coffee

14:45-15:00: Andy Sutherland, Chair of the Leeds/Bradford Group

50^th Anniversary of the local group

15:00-15:40: Vinny Davies, School of computing science, University of Glasgow

Spot the Difference: Statistics, Data Science, Machine Learning, AI.

In recent years, the field of influence of Statistics has been affected by the popularisation of more trendy subject areas such Data Science, Machine Learning and Artificial Intelligence. While many of the methods used have striking similarities, industry and funders often neglect Statistics, prioritising the latest trends and buzzwords. In this talk, I will discuss the differences and similarities of these subject areas and look at how Statistics can stay relevant in the modern world where buzzwords rule.

15:40-16:20: Owen Johnson, School of Computing, University of Leeds

It's all going horribly wrong - temporal challenges in health data

Using the past to predict the future has always been a flawed method. Enthusiasm for data science linked to embedded AI within IT systems risks unintended consequences. In healthcare data quality, veracity and consistency varies across geography, organisations and time so the challenges are particularly complex and serious. New and promising multi-disciplinary methods are emerging but if the past history of healthcare IT is anything to go by we can predict some major disasters ahead.

16:20-17:00: Kaylea Haynes, Peak Information Technology and Services, Manchester

Where is the AI?

With new machine learning tools and AI techniques coming in to the scene this talk will show examples of where statistics still have a place in a data scientist’s tool box.