English

This course focuses on contemporary global health issues, with emphasis placed on contextualizing global, national, and subnational health disparities. Students study how health, the environment, and sustainable development are interrelated and work to improve our understanding of both policy and practice. Therefore, students are introduced to important actors and their stated goals. Students examine cases from low- and middle-income countries, as well as from high-income countries. The course takes an interdisciplinary approach, using various perspectives including sociology, bioethics, epidemiology, anthropology, clinical medicine, and political science. 

Topics covered in this course include writing and debugging software for hardware, working with microcontrollers, sensors, motors, key theoretical concepts for robotic systems, the scientific method applied to robotic systems, and scientific reporting and writing.

This course develops students' understanding of the fundamental principles needed to design drinking water supply and treatment works, wastewater collection and treatment systems, and their ability to develop waste and resource management strategies. 

This course examines theoretical approaches, empirical methods and findings in second language acquisition research. Topics include features of language learners and their learning processes (e.g., age, crosslinguistic influences), individual influences on language development (e.g., motivation, aptitude) and social dimensions (e.g., identity, community). 

This course introduces a variety of basic principles, techniques, and modern advances in text mining. Topics to be covered include basic natural language processing techniques, text representation, text classification, feature selection, text clustering and topic models, word embedding, deep neural networks and introduction to (large) language models. During the course, students actively learn how to apply text mining methods to data analysis and how to use them together with natural language processing and machine learning techniques on real data problems. The course has a strong practical focus: students gain hands-on experience in Python by applying the methods to real data during the course and interpreting the results. The course provides an understanding of the principles, problems, techniques, and solutions associated with text mining and to enable them to gain knowledge of how recent advances in text mining relate to innovative approaches to organizing, characterizing, finding and exploiting large amounts of textual information in the search for new knowledge. Assumed knowledge includes basic knowledge/motivation in programming and data science.

This course provides an opportunity for students to generate a strategic international market entry plan, considering the company’s characteristics and market conditions. Prior knowledge is required of international market and country risk assessment. 

The course covers international expansion strategies; the international business ecosystem, and systems thinking for business. 

This course examines the composition of the cultural heritage of Mexico drawing from the material characteristics of the objects and sites to distinguish between tangible heritage and cultural practices that permit the identification of intangible heritage. 

This course introduces advanced empirical industrial organization methods and quantitative research methods on unstructured data. It covers a broad spectrum of models, consisting of both conceptual seminars and programming practices.
 

This course offers a comprehensive introduction to the politics, strategy, and international security implications of nuclear weapons. Beginning with the science and history behind the bomb's invention, the course explores how nuclear weapons have shaped global politics from Hiroshima to the contemporary era of multipolar nuclear rivalry. Through close engagement with foundational texts and contemporary policy debates, it examines the logic of deterrence, the strategic dilemmas of extended deterrence, and the evolving architecture of non-proliferation and arms control. The course applies these frameworks to key cases—such as NATO, Iran, Russia, China, and France—to assess the challenges of escalation, proliferation, and technological change in the emerging nuclear age. Emphasis is placed on bridging theory and practice, enabling students to critically assess how nuclear weapons continue to structure conflict, diplomacy, and international order in the 21st century.

This course is driven by time series perspectives and short term statistical behavior, underpinned by digital signal processing and machine learning, which is appropriate for cash assets (stocks, bonds, currencies, portfolios) and futures. It therefore answers the needs of the rapidly changing global financial system which requires financial models to be adaptively inferred from the data. Students gain hands-on experience through structured Python assignments based upon time series models, robust estimators, subspace techniques, portfolio optimization, and data analytics on graphs. There are no particular prerequisites, although knowledge of estimation theory and statistics would be useful.