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Machine Learning for Global Development
Last Updated: 2026-02-05 15:35:41
Abstract
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.
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 , MSC
- Frequency
- Yearly recurring
Examination
- Type
- graded semester performance
Registration & Places
- Max Places
- 24
Course Components
| Type | Title | Time & Place | Hours |
|---|---|---|---|
| lecture with exercise |
Machine Learning for Global Development
Does not take place this semester.
The course has been canceled for the autumn Semester 2020 and will be offered during the spring semester 2021.
|
No time listed | 2 h weekly |
Offered In
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GESS Science in Perspective (Only the topics listed in this paragraph can be chosen as GESS Science in Perspective. Further below you will find the "type B courses Reflections about subject specific methods and content" as well as the language courses. 6 ECTS need to be acquired during the BA and 2 ECTS during the MA Students who already took a course within their main study program are NOT allowed to take the course again.)
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Type B: Reflection About Subject-Specific Methods and Contents (Subject-specific courses: Recommended for doctoral, master and bachelor students (after first-year examination only). Students who already took a course within their main study program are NOT allowed to take the course again. These course units are also listed under "Type A", which basically means all students can enroll)
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Doctoral Department of Humanities, Social and Political Sciences (More Information at: )
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