Adina Rom: Katalogdaten im Frühjahrssemester 2021
|Frau Dr. Adina Rom
Professur für Entwicklungsökonomie
ETH Zürich, CLD B 6
|+41 44 633 85 65
|Geistes-, Sozial- und Staatswissenschaften
|ETH Sustainability Summer School
|C. Bratrich, P. Krütli, A. Rom, E. Tilley, C. Zurbrügg
|The ETH Sustainability Summer School provides young researchers with the opportunity to work on current and sustainability-related topics in interdisciplinary and intercultural teams. Focus is given not only to teaching theoretical knowledge but also to solving specific case studies.
|Within ETH Zurich's Critical Thinking Initiative (CTI), students further develop their critical thinking and communications skills including: the capability to analyse and reflect critically, to form an independent opinion and develop a point of view, as well as to communicate, argue and act in an effective and responsible manner.
Based on this concept, the ETH Sustainability Summer School is providing its students with the following qualifications and learning outcomes:
- Interdisciplinary and multicultural competence: Students gain basic knowledge in scientific disciplines beyond their own and learn how to work effectively in interdisciplinary and multicultural teams.
- Methodological competence: Students gain basic knowledge of different scientific methods beyond their selected study discipline.
- Reflection competence: Students learn to critically reflect their own way of thinking, their own research approaches, and how academia influences and interacts with society at large.
- Implementation skills: Students will apply creative technologies in solution finding processes to gain knowledge and prototyping-skills to increase hands-on experience by applying knowledge in concrete cases.
This year's event on solid waste management is a collaboration between ETH Sustainability, the ETH for Development (ETH4D) Initiative and Kwame Nkrumah University of Science and Technology (KNUST, Kumasi, Ghana), and will take place at KNUST in Kumasi, Ghana.
To find more information and to register, visit our website: https://ethz.ch/en/the-eth-zurich/sustainability/education/summer-and-winter-schools/ETHSustainabilitySummerSchool.html
|further information and registration:
|Voraussetzungen / Besonderes
|The Summer School 2021 is a collaboration between ETH Sustainability and the ETH for Development (ETH4D) Initiative. It provides students and young researchers the opportunity to develop and test solutions for a real-world challenge related to solid waste. Students will work in interdisciplinary teams. The summer school will be organized as a hybrid version, held in parallell at ETH Zürich and at Kwame Nkrumah University of Science & Technology (KNUST) in Kumasi / Ghana.
We will invite Bachelor, Master and PhD students from ETH Zurich and students from KNUST with a wide range of backgrounds and disciplines.
Candidates will be selected from all disciplines. Submitting a motivation letter and CV is a prerequisite for the application. Applicants will be evaluated on their academic strength, creativity, technical-related expertise, and their dedication to contribute to solving the world's most pressing challenges.
Depending on the Covid-19 situation, the course might have to be cancelled or its format may change.
|Machine Learning for Global Development
Number of participants limited to 24
Prerequisite: Students on BSc or MSc level who have already successfully participated in a data science and programming course.
|J. D. Wegner, L. Hensgen, A. Rom
|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.
|Voraussetzungen / Besonderes
|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).
|International Development Engineering
|I. Günther, A. Rom, K. Shea, E. Tilley
|In this seminar, students will learn from researchers around the globe about technological interventions designed to improve human and economic development within complex, low-resource setting. Students will also get familiar with frameworks from social sciences and engineering, helping them to understand, and evaluate the discussed technologies and to put them into a broader context.
|• Students will get familiar with frameworks from social sciences and engineering needed for innovation in a complex, low-resource setting.
• Students will learn about concrete examples of technological interventions designed to improve sustainable development and critically reflect on them.
• Students get a broad understanding of some of the most important issues and discussions related to global sustainable development.
|In the introductory class, students will learn about challenges related to global sustainable developments and how they have developed over time. Students will then get exposed to frameworks from social sciences and engineering disciplines, which will help them analyze technologies designed for low-resource settings. In the remaining sessions thought leaders from the field of development engineering will present a wide range of innovations from sectors such as health, water and sanitation, education and governance that will then get discussed with students using the frameworks they learned.