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Machine Learning for Global Development
Last Updated: 2026-02-05 16:21:54
Abstract
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.
Objective
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.
Content
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.
General Information
- Language
- English
- Levels
- DS , DR
- Frequency
- Yearly recurring
Examination
- Type
- graded semester performance
Registration & Places
- Max Places
- 40
- Signup End
- 04.03.2023
Course Components
| Type | Title | Time & Place | Hours |
|---|---|---|---|
| lecture with exercise | Machine Learning for Global Development |
|
2 h weekly |
Offered In
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Science in Perspective (In “Science in Perspective”-courses students learn to reflect on ETH’s STEM subjects from the perspective of humanities, political and social sciences. Only the courses listed below will be recognized as "Science in Perspective" courses.)
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Type B: Reflection About Subject-Specific Methods and Contents (Subject-specific courses. Particularly relevant for students interested in those subjects. All these courses are also listed under the category “Typ A”, and every student can enroll in these courses.)
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Doctorate Humanities, Social and Political Sciences (More Information at: )