English

This course is designed to train students in the basic skills of archaeological post-excavation, processing, and results dissemination. It explains the varied methods used by archaeologists to analyze and process different types of archaeological material and provide experience in a number of necessary skills. These skills may include washing and numbering of artifacts, basic conservation, artifact illustration and cataloguing, sample washing and sorting, sample sieving, sample flotation, inking-up and digitizing of excavation drawings. This course includes standard lectures, laboratory-based talks, physical demonstrations, and hands-on experience. The course also explores how and where to publish results, and interaction with the media and the public.

This course introduces data science techniques to harness financial data for making sound financial decisions or answering questions of financial interests. It combines tools used in a variety of fields (finance, economics and statistics). Students will finish the course equipped with a workman’s familiarity with the tools of financial data science, facility with financial data handling and statistical programming, and—hopefully—a good understanding of what decisions you want to make, or what questions you want to ask and how best to do it with econometric tools and financial data.

This course introduces the nature of economic problems and how society and man deal with the economic problems. It covers resource allocation and the concept of economic efficiency, product and factor markets, macroeconomic indicators and the nature of macroeconomic problems, and macroeconomic policy. Economic concepts and theories are applied to various social and economic issues such as marriage, crime and government policies.

This course provides students with the knowledge and skills commonly adopted in practice to implement Enterprise Risk Management (ERM) framework utilizing risk management qualitative and quantitative methods. The course addresses how to identify, assess, control, and respond to risks for an enterprise and also integrates risks with business strategy to improve performance. In addition, the course identifies environmental, social, and governance (ESG) related risks to be incorporated into ERM framework. Last, this course introduces the practical implementation and application of financial risk management covering market, liquidity, credit, and operational risk measurement and management from the viewpoint of financial institutions.

This course explores key sustainable development challenges, including climate change, poverty, inequality and social justice. It explores the concept of sustainable development and assesses the effectiveness of a range of approaches to development. The role of government, business and civil society in addressing global challenges is considered.

What are the roots of our concern for the environment? What did environmental activism look like in the 1960s, the 1930s or even the 1870s? This course offers a survey of where environmentalism has come from and where it is going. This course provides students with a deeper appreciation for the history of environmentalism. We learn about links between the development of the sciences of the environment and environmentalism as a social movement. The geographical focus in this course is on Europe and North America. However, students also locate and interrogate how environmental concern and policy has developed in various parts of the globe. Students note the experiences and contributions of different identity groups. In doing so, they consider the impact of and reactions to European imperialism and postcolonial globalization. They also examine and critique the role of the United Nations and other international organizations in environmental affairs.

This course covers the history of western philosophy from the Greek era to the present and changes in debates and concepts of major philosophers.

This course introduces the basic theories, model architectures, algorithms, and implementation of deep learning for computer vision. Students obtain hands-on experience on implementing and training deep neural networks for computer vision tasks. The course covers the following topics: (1) neural network optimization algorithms; (2) backbone network architectures for computer vision, including convolutional neural networks and transformers; (3) network structure design for visual recognition tasks (image classification, object detection, image segmentation), and visual content generation tasks; (4) implementation and training of neural networks for computer vision tasks; (5) advanced topics in computer vision and deep learning. 

This course examines the rise of human rights discourse and its relationship to other discourses on suffering and social justice. It focuses on the experience of victims of human rights abuse and the politics of meaning. Students will engage in critiques of law as a reductionist discourse on the social by exploring the relationships between human rights and cultural differences such as gender, ethnicity, religion and indigenous cultures. The embodied self, social interdependency and the architecture of social institutions are the backdrop through which the course explores the tensions between universal and relativist understandings of human rights and their realization. Students will be introduced to the fundamentals of human rights, the global human rights machinery, and the ethics of humanitarian intervention, and will consider how sociologists have studied and written about human rights.