In this course students will learn theories of machine learning and its application to problems 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 objective of this course is to introduce students with a non-technical background to machine learning. Emphasis is on hands-on programming and implementation of basic machine learning concepts to demystify the subject, equip participants with all necessary insights and tools to develop their own solutions, and to come up with original ideas for problems related to the context of global development. Specific importance is placed upon the reconciliation of the predictions, which have been generated by automated processes, with the realities on the ground; hence the linkage between technical and social issues. This raises questions such as “In how far can we trust an algorithm?”, “Which factors are hard to measure and therefore not integrated in the algorithm but still crucial for the result, such as cultural and social influences?”. These questions will be discussed in the interdisciplinary group, equipping students with various perspectives on this crucial and very current debate.
This course will give an introduction to machine learning with emphasis on global development. We will discuss topics like data preprocessing, feature extraction, clustering, regression, classification and take some first steps towards modern deep learning. The course will consist of 50% lectures and 50% hands-on programming in python, where students will directly implement learned theory as a software to help solving problems in global development.
Prerequisites / Notice
This course will give an introduction to machine learning with emphasis on applications in global development. It will consist of 50% lectures and 50% programming exercises (in python). Teaching assistants from the EcoVision Lab will help with all programming exercises without any needs for additional funding.
Students should bring their laptops to the exercises because we will program on laptops directly.
It is required that students enrolling in this course have successfully passed a course that deals with basic data science and are familiar with programming (preferably in Python).
Performance assessment information (valid until the course unit is held again)