English

The Reformation, which began with Martin Luther in early sixteenth-century Germany, was one of the great turning-points of modern European history, splitting Catholic Christendom and giving rise to many different strands of Protestantism. Using primary sources extant from the period, in English translation if necessary, this course addresses this development in a mixture of lecture and seminar formats. With a broad chronological span, and a geographical scope stretching across much of Western Europe, it offers the stimulating intellectual challenge of learning how to relate key theological concepts to the experiences of the people, in all their diversity.
 

From the Internet to the WWW, from wireless communication networks, large power networks to global transportation networks, from the brain in organisms, various metabolic networks to various economic, political, and social relationship networks, people's socioeconomic activities and daily life all take place in a world full of complex networks. Complex network theory studies the commonalities between various complex networks that appear to be different from each other and the universal methods for dealing with them. Since the end of the 20th century, complex network research has permeated many different fields from mathematics and physics to life sciences and information engineering. The scientific understanding of the quantitative and qualitative characteristics of complex networks has become an extremely important challenge in scientific research in the network era. This course will be taught in English, and strives to introduce the basic concepts, basic theories, basic algorithms and practical applications of network science represented by complex network theory in a way that science and engineering undergraduates can understand, including some of the lecturer's own research. The main purpose is to enable students to understand the basic system of complex network systems through the study of this course, master the basic concepts of complex network theory, and cultivate students' interest in network science.

The course explores some of the principles and doctrines underlying the criminal law. It investigates some of the theoretical (and particularly, ethical) problems that criminal law raises. The course increases students’ understanding of many of the principles underlying the criminal law, especially those concerning the scope of criminal prohibitions and the criteria for attributing responsibility and blame to individual wrongdoers. With increased understanding of those principles, students learn to integrate analysis of general issues and principles with argument about particular rules and doctrines in the criminal law.

This course develops awareness of the complex relationship between spoken language and society through discussion of existing literature and through experience of experimental paradigms used to study spoken language variation. It focuses on the study of phonetic aspects of accent variation and change and so it is assumed that students are familiar with basic concepts in phonetics and phonology.  

This course explores the relationship between landscape-scale spatial patterns and the ecological, physical, and social process that drive environmental change. It then applies this to real-world problems to achieve sustainable landscapes in the context of biodiversity conservation, ecosystem services, and social-ecological outcomes.

We focus on themes including the historical review of transatlantic relations, distinct functional modules in policies and interactions encompassing their overall and regional strategies, security, economics, technology, digital, global governance and economic models from the beginning of the 21st century to the present.
Students are expected to achieve three major goals: (1) to better understand the evolution of the transatlantic alliance; (2) to discover the causes of cooperation and divergences on different issues among US, Europe and China; (3) to learn about how Chinese academia views the US and Europe.

The course provides an overview of the relationships between computing systems and human beings, from a technological perspective. The first weeks introduce the main theoretical and technical concepts of human-computer interaction (HCI), such as cognitive aspects of visual design, interaction design, persuasion, and user experience. The students analyze the risks and possibilities associated to computing interfaces, wearable technologies, and data visualization. The second part of the course focuses on AI and algorithms, with a broad introduction to the main techniques and challenges involved, e.g., machine learning and data science. In this part as well, once equipped with the basic conceptual tools, students focus on the ethical challenges of modern AI systems, with a discussion on the concepts of accountability and trust?

This course examines a pertinent challenge of humankind: how to feed 12 billion people while maintaining the integrity and function of our planet. It challenges participants with contrasting viewpoints for a nuanced understanding of the multidimensional aspects of food production and consumption. Course participants explore the food debate as consumers and scholars, with focus on the science behind innovation of food and food systems, locally and globally. Course participants map the future of food and agriculture with view of the UN's Sustainable Development Goals.

This course focuses on understanding organizations in terms of structures, shared beliefs, identities and practices, concepts of efficiency and power and the implications of these insights for how we intervene to change organizations. The course helps students build their understanding of organizing beyond simplistic, functional frameworks and provides them with the necessary sociological and psychological concept to help them make sense of why organizations act in certain ways.

Reinforcement learning (RL) refers to a collection of machine learning techniques which solve sequential decision making problems using a process of trial-and-error. It is a core area of research in artificial intelligence and machine learning, and provides one of the most powerful approaches to solving decision problems. This course covers foundational models and algorithms used in RL, as well as advanced topics such as scalable function approximation using neural network representations and concurrent interactive learning of multiple RL agents.