ionaei/ML4Health-Zimbabwe-2026

# Machine Learning for Health Applications, Zimbabwe 2026 Welcome to the **Machine Learning for Health** module. This course introduces the **foundations of machine learning**, with a strong focus on: - Linear models - Regularisation - Model evaluation - Ensemble methods - Application to real-world health data The course builds from first principles and culminates in applying machine learning methods to health-related prediction tasks. This course is a spin-off of the ML4NS programme developed within the Department of Brain Sciences at Imperial College London. --- ## Teaching Team Iona Biggart AI 4 Paediatrcis PhD student, Department of Brain Sciences, Imperial College London (iona.biggart23@imperial.ac.uk) Marco Reed AI 4 Paediatrcis PhD student, Department of Brain Sciences, Imperial College London (marco.reed24@imperial.ac.uk) Kevin Meck Biomedical and applied medical AI engineer at Neotree Aditi Rao Doctoral candidate at Imperial College London with Neotree ## Guest Lectures Professor Payam Barnaghi Chair in Machine Intelligence Applied to Medicine, Department of Brain Sciences, Imperial College London Professor Tawanda Mushiri Associate Professor, AI and Robotics, SIRDC Antigone Fogel AI for dementia research, UK DRI and Department of Brain Sciences, Imperial College London --- # Course Overview Machine learning is transforming modern healthcare. From disease prediction to risk stratification and treatment modelling, robust statistical learning methods are central to evidence-based medicine. This course focuses on **core machine learning methods** that form the backbone of applied health data science. We concentrate on: ### 1️⃣ Linear Models - Linear regression - Logistic regression - Bias–variance tradeoff - Regularisation (L1 / L2) - Model interpretation - Feature importance - Evaluation metrics Linear models are essential because: - They are interpretable - They are statistically grounded - They often …