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The field of Artificial Intelligence has seen major breakthroughs in recent years, offering unprecedented possibilities in addressing major global health challenges, particularly in health equity and access. This foundational course in AI for Global Health aims to provide students with the theoretical basis and practical skills needed to apply machine learning techniques to healthcare innovations for global public health problems. The course does not require prior background in machine learning or global public health. It takes a unique approach by teaching foundational machine learning topics using case studies and real-life data from global health innovation projects across Africa, South Asia, and Latin America. Students will learn machine learning topics alongside weekly case studies involving datasets from projects on Malaria, Maternal Health, Neonatal Health, Primary Health Care, Cancer, and more. The course covers topics from basic machine learning to advanced techniques like convolutional neural networks, generative AI, and time series/sequence data. Labs and mini-projects will involve applying these methods to real datasets, culminating in a final challenge project based on a real-life multi-country implementation in East Africa. Prerequisites: Programming knowledge in a high-level language such as Python.