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Course catalogue

Find individual courses here or browse the curricula of our graduate and executive programmes.
For the PhD programmes, please find more information here.

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GRAD-C22

Course-ID: Causal Inference
Instructors: Dr. William Lowe

Abstract

This course covers contemporary methods for causal inference with a focus on applications to public policy topics and connections to data science and machine learning techniques.

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GRAD-C23

Course-ID: Math for Data Science
Instructors: Prof. Asya Magazinnik, PhD

Abstract

This course aims to deliver a compact and tailored introduction to the core mathematical concepts of data science. Linear algebra, probability theory, statistics, and optimisation are mathematical pillars underlying the practice of data science. The course covers foundational mathematical concepts such as statistical estimation, norms, matrix algebra, Lagrange Multipliers and many more in theory and practice. Upon completing the course, students will have a broad knowledge of linear algebra, probability theory, statistics, and optimisation necessary to understand the theoretical underpinnings of modern statistics and machine learning methods.

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GRAD-C24

Course-ID: Machine Learning

Abstract

This course provides an introduction to the world of machine learning. By the end of this course students will have a sound understanding of the key concepts of machine learning, analyze data using some of its main methods, and a solid foundation for more advanced or more specialized study. The course covers standard topics in supervised and unsupervised learning, including the most common learning algorithms for regression, classification and clustering, but also touches on advanced topics in machine learning particularly important for public policy, such as uncertainty quantification. Students will learn the fundamental concepts underlying machine learning algorithms as well as the practical use of machine learning algorithms using open-source frameworks.

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GRAD-C9

Course-ID: Data Structures & Algorithms

Abstract

This course provides a comprehensive introduction to the basics of computers, data structures, and algorithms. The first half of the course explores the core principles of computer logic and various data types and structures. Building on this foundation, the second half covers essential algorithms and their real-world applications, particularly within the field of public policy. Theoretical concepts are reinforced through practical implementation in Python, following industry-standard software development practices and paradigms to enhance understanding.

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GRAD-E1023

Course-ID: Organisational Failure & Public Policy Disasters
Instructors: Wolfgang Seibel

Abstract

The COVID-19 pandemic has revealed different degrees of vulnerability and resilience of government and public administration across many nations. We have good reasons to acknowledge that public and private governance and organisational structures under stress reveal latent fault-lines and weaknesses that otherwise would have remained undetected. Successful crisis management, however, rests on early warning and sufficient levels of preparedness known in the literature as high reliability requirements. The question how and why these requirements are frequently not fulfilled even under relatively favorable conditions is obviously relevant when it comes to learning and prevention. This course addresses that very question through instructing students in making in-depth inquiries into complex cases of ill-fated policies and organisations based on causal process tracing and a chosen theoretical framework. The subject is the analysis of organisational failure and public policy disasters from a variety of perspectives in an attempt to enable students to assess the risks of failure and to contribute to appropriate risk reduction and crisis management strategies. The Empirical Cases to be analysed fall into three different categories, (I) Socio-technical Systems, (II) International Organisation and Security Agencies and (III) Civil Administration. (I) The Gulf of Mexico oil spill of 2010 / The Fukushima Accident of 2011 / The Federal Aviation Administration's Aircraft (FAA) Certification Process and the case of Boeing 737Max. (II) The collapse of the UN mission and the subsequent genocide in Rwanda 1994 / The inability of the international community to protect the UN "safe area" / Srebrenica during the Bosnian war in 1995 / The failure of US security services prior to 9/11 to analyse the terrorist threat and to prevent the attack of September 11, 2001. (III) The Break-Down of Disaster Relief in the Aftermath of Hurricane Katrina 2005 / The Hillsborough Disaster 1989 / The Fire at Grenfell Tower in London 2017.

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GRAD-E1305

Course-ID: Topics in Economic Policy
Instructors: Prof. Dr. Christian Traxler

Abstract

This course discusses topics in economic policy. We will draw on advanced contributions from research in Economics and maintain a clear microeconomics focus on consumers and households (and, to a lesser extent, firms) responses to governmental interventions in different markets.

The first half of the course will examine fiscal stimulus policies, such as temporary VAT cuts, "helicopter money", consumption subsidies, or car-scrapping programs (e.g., the US cash-for-clunkers program or the Germany counterpart, Umweltpraemie).

Using results on the environmental impact of car-scrapping programs as point of departure, the second half of the course will analyse further economic policies and regulatory tools to accelerate the "green transition" (e.g., subsidies for e-vehicles, public transport or solar panels). We will discuss policy evaluations that assess the efficacy of these instruments as well as their fiscal, environmental but also distributional implications.

Throughout the course, all questions will be approached based on research papers employing state-of-the-art empirical tools that are common in quantitative impact assessments and policy analyses. We will reserve specific session slots that are devoted to policy discussions. In some of these discussions, we will bring in practitioners and applied researcher working on these policy topics.

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GRAD-E1394

Course-ID: Deep Learning
Instructors: Prof. Lynn Kaack, PhD

Abstract

As the fastest growing subfield of machine learning, deep learning is the technology behind facial recognition, machine translation, AlphaGo, and many other well-known applications. As policy makers are beginning to regulate machine learning, technical understanding of deep learning in public policy is invaluable. In policy research and analysis, deep learning has only recently started to be applied. In this course, students will learn the main theoretical concepts of (deep) neural networks, and get introduced to applications in computer vision, natural language processing and other areas. Students will gain hands-on experience by training and testing their own models in policy-relevant applications. The main objective of this course is to enable students to scope out new meaningful and robust deep learning applications, and to advise decision makers on strengths and limitations of the technology.

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Master of Data Science for Public Policy

Deep Learning

Course-ID: GRAD-E1394

Abstract

As the fastest growing subfield of machine learning, deep learning is the technology behind facial recognition, machine translation, AlphaGo, and many other well-known applications. As policy makers are beginning to regulate machine learning, technical understanding of deep learning in public policy is invaluable. In policy research and analysis, deep learning has only recently started to be applied. In this course, students will learn the main theoretical concepts of (deep) neural networks, and get introduced to applications in computer vision, natural language processing and other areas. Students will gain hands-on experience by training and testing their own models in policy-relevant applications. The main objective of this course is to enable students to scope out new meaningful and robust deep learning applications, and to advise decision makers on strengths and limitations of the technology.

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Master of Data Science for Public Policy

Introduction to Data Science

Course-ID: GRAD-C11

Abstract

This course will introduce you to the modern data science workflow with R. In recent years, data analysis skills have become essential for those pursuing careers in policy advocacy and evaluation, business consulting and management, or academic research in the fields of education, health, and social science. We will cover topics like version control (Git) and project management; data collection, wrangling, storage, and visualization; model fitting; advanced workflow issues, debugging, automation; and data science ethics.

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Master of Data Science for Public Policy

Math for Data Science

Course-ID: GRAD-C23

Abstract

This course aims to deliver a compact and tailored introduction to the core mathematical concepts of data science. Linear algebra, probability theory, statistics, and optimisation are mathematical pillars underlying the practice of data science. The course covers foundational mathematical concepts such as statistical estimation, norms, matrix algebra, Lagrange Multipliers and many more in theory and practice. Upon completing the course, students will have a broad knowledge of linear algebra, probability theory, statistics, and optimisation necessary to understand the theoretical underpinnings of modern statistics and machine learning methods.

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Master of International Affairs

Global Politics

Course-ID: GRAD-C1-2000

Abstract

This course gives students a theory-driven introduction to global politics, focusing on how states approach governance problems that they cannot solve alone, and on the international institutions created to provide public goods. The guiding theme is the contestation of global authority and governance structures. By the end of the course, students will be able to understand and explain the trajectory and the challenges of global politics, from a political science perspective. They will be able to review academic literature relating to global politics, formulate research designs, and conduct basic research at M.A. level. The goal is to practice evidence-based scientific arguments on these topics. The course will also provide insights into various topics that relate to the three streams of the Master of International Affairs.

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Master of International Affairs

International Law

Course-ID: GRAD-C2-1101

Abstract

This course aims to provide a foundational basis in international law for Masters in International Affairs students. The course covers the structure and the sources of international law, and the operation of international law in selected substantive areas covering international security, human rights, migration, global economy and the climate crisis.

The course has two parts:

Part I surveys the structure, nature and central characteristics of modern international law, the relationship between international law and domestic law, modes of international law making, debates on the hierarchy of norms and norm conflicts in international law, subjects of international law and enforcement mechanisms in international law, covering international courts and tribunals as well as compliance committees.

Part II focuses on selected substantive areas in international law. These include international law on the use of force, international law of armed conflict, international crimes and international criminal law, international economic law, international human rights and refugee law, state responsibility in international law, and international law and climate change.

The course does not assume any prior knowledge of international law or legal training.

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