851-0648-00L  Machine Learning for Global Development

SemesterSpring Semester 2021
LecturersJ. D. Wegner, L. Hensgen, A. Rom
Periodicityyearly recurring course
Language of instructionEnglish
CommentNumber of participants limited to 24

Prerequisite: Students on BSc or MSc level who have already successfully participated in a data science and programming course.


851-0648-00 GMachine Learning for Global Development Special students and auditors need a special permission from the lecturers.
This course will be offered in the Spring Semester 2021 as an exception - it is usually scheduled in the Autumn Semester
2 hrs
Thu08:15-10:00HG G 26.5 »
J. D. Wegner, L. Hensgen, A. Rom

Catalogue data

AbstractIn 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.
ObjectiveThe 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.
ContentThis 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 / NoticeThis 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

Performance assessment information (valid until the course unit is held again)
Performance assessment as a semester course
ECTS credits3 credits
ExaminersJ. D. Wegner, L. Hensgen, A. Rom
Typegraded semester performance
Language of examinationEnglish
RepetitionRepetition only possible after re-enrolling for the course unit.

Learning materials

No public learning materials available.
Only public learning materials are listed.


No information on groups available.


General : Special students and auditors need a special permission from the lecturers
Places24 at the most
Waiting listuntil 08.03.2021

Offered in

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Science, Technology, and Policy MasterElectivesWInformation