The AI4Good course is a hackathon turned into a full course. At the beginning, stakeholders active in the development sector will describe several problems that could be solved with a machine learning approach. Students will spend the semester on designing, implementing, and testing suitable solutions using machine learning. Progress will be discussed with all course members.

Doctoral candidates from all ETH departments, whose research is related to global sustainable development issues, and conducting research in low- or middle-income countries are invited to give a presentation about their on-going work and discuss their doctoral project with a diverse group of researchers.

Introduction to basic concepts and methods of digital image processing.

The objective of this lecture is to introduce the basic concepts of image formation and explain the basic methods of signal and image processing.

This course gives an introduction to machine learning and its application in the context of global development, with a focus on developing countries (e.g., predicting the risk of child labor or chances of a malaria outbreak). By the end of the course, students will be able to critically reflect upon linkages between technical innovations, culture and individual/societal needs.

The aim of the course is to provide a hands-on experience with close-range photogrammetry. The students will go through all aspects of 3D reconstruction starting with the image acquisition, camera calibration, automatic sparse geometry reconstruction, and eventually produce a final textured 3D model.