A hands-on course for students and researchers at the intersection of statistics, probabilistic programming, and population health.
One of the groundbreaking advances in machine learning research in the past decade is surrounding the emergence of increasingly sophisticated, robust, and easily usable probabilistic programming languages. These new tools, including Stan or numpyro, hide tedious calculations involving automatic differentiation and gradient-based optimization from the end-user, making modern statistical methods widely available to data scientists in Africa that wish to address some of the most urgent challenges on the continent, ranging from habitat degradation, air pollution, extreme weather events, disease outbreaks and population health in general.
This one-week course will cover how you can integrate modern statistical techniques with the Stan probabilistic programming language to effectively address a broad range of applications from epidemiological, genomic and spatial data. We hope this course will equip you with intelligence-driven statistical technologies to drive your own evidence-based discoveries in global health or other applications, and more broadly increase your fluency in artificial intelligence and modern statistics.
What attendees will learn
- Bayesian workflow with probabilistic programming in Python and Stan
- Core regression models for hierarchical data
- Gaussian process (GP) regression
- State-of-the-art GP approximations for scalable inference
- Infectious disease modelling
- Pathogen phylogenetics
Organised by: Department of Mathematics, Imperial College London; the Machine Learning and Global Health Network; and the African Institute for Mathematical Sciences
For more information and to apply please visit https://mlgh.net/ms_ml_short_course/overview/
English