Korea, South

This course explores the transformative power of media. Through critical analysis and hands-on media design, students examine how media artifacts construct national identities, deploy soft power, challenge gender norms, and transform digital spaces into sites of justice. Bridging theory and practice, students develop ethically grounded media interventions, such as storyboards, TikTok campaigns, and justice-oriented projects, etc. that engage with the tensions between cultural specificity, global algorithms, and neoliberal platforms. 

This course analyzes the changing roles and functions of museums in a digital era. Students examine virtual museums, mobile applications, e-learning, and digital strategies. We also explore trends and horizons of museum technology to shape a museum of the future. Students complete article reviews and a project for a better understanding of the museum of our age. 

Topics include What is a museum, Museums in the digital age, Museum informatics, Digital collections management, Digital preservation, 3D applications in museums, Interactive museums, Case studies, Trends, HCI in the museum context, Virtual museums. 

This course focuses on the legal responses to climate change in three contexts: international, comparative, and national laws. It begins with causes and effects of global climate change and the methods available to control and adopt to it. It then investigates the emergence of climate change regime and various policy tools nations employ, including emission trading, carbon tax, litigation, securities disclosures, and voluntary action. Relations with other legal regimes (e.g., human rights, trade, and environmental justice will also be examined. 

Topics include Climate change and international law, Evolution of United Nations climate change regimes, Kyoto Protocol, Paris Agreement, Paris rulebook, Climate governance beyond the UN, Net zero, Green New Deal, Energy and climate change, corporate responsibility, climate liability, plastic pollution.  

This course explores the historical development of football in England and its broader social, political, and economic contexts to provide a deeper understanding of modern sport culture and industry. From its origins in public schools and working-class communities to the globalization of the Premier League, changes in club ownership, fan culture, and media dynamics, the course examines a wide range of topics including how football has changed and exerted influence within social contexts such as imperialism, class, popular culture, and media. By engaging with key moments and transformations in British football, students gain critical insights into the structures and issues that shape the contemporary sport culture and industry. 

This introductory course focuses on the fundamentals of investment analysis and asset pricing. The course examines modern portfolio theory, the capital asset pricing model, arbitrage pricing theory, corporate and government securities, stock and bond valuations, and derivatives. Students are equipped with a foundational understanding of various investment alternatives and the methods used to assess their value. 

Topics include Investment environment and financial instruments, Capital allocation to risky assets, Efficient diversification, Capital asset pricing models, Arbitrage pricing theory, Bond prices and yields, Term structures, Equity valuation models, Option markets, Option valuation, Future markets.  

This course delves into the theoretical underpinnings and practical applications of deep neural networks. Deep learning has revolutionized industries ranging from healthcare to finance, driving advancements in natural language processing, computer vision, and autonomous systems. 

From understanding fundamental concepts to implementing advanced architectures like convolutional and recurrent networks and transformers as well, this course covers both theoretical knowledge and hands-on experience essential for navigating the complexities of deep learning. 

Topics include Deep learning basics, Neural networks, Training neural networks, Convolutional neural networks, Recurrent neural networks, Transformers, Applications: NLP, Applications: CV, Generative models. 

In today's world, data-driven science is paramount, and biology is no exception. This course delves into the principles of data-driven biology, exploring platform technologies and their applications across various domains, including genomics, transcriptomics, proteomics, interactomics, and other 'omics ' branches of biology. 

Students engage in discussions about the influence of 'omics' on human disease research and medicine. This course helps students to understand the latest trends in data-driven bio research and forecasts for the future bio industry. The course builds fundamental understanding and application skills in various omics technologies, and explores the past, present, and future of genomic medicine in relation to paradigm shifts in healthcare.  

The key topics of the course include the following: 1. Introduction of Omics and data-driven biology 2. Genome Projects 3. Next-generation sequencing technology (NGS) 4. Transcriptomics with DNA-chip and NGS 5. Proteomics with Mass Spectrometry 6. Variomics (human genetic variation, genotype-to-phenotype) 7. Pharmacogenomics 8. Epigenomics 9. Regulomics 10. Interactomics (molecular interactions) 11. Metagenomics (Microbiomics) 12. Single-cell Omics (Single Cell Transcriptomics) 13. Cancer Genomics 14. Cancer Immunogenomics. 

Prerequisites: General Biology, Biochemistry, Genetics 

This course considers how stories are told in an age of digital media. For the first half of the semester, we focus on key terms and concepts in narrative theory and practice, applying these terms to different narratives. We look at some good examples from various media and explore their narrative structures with each application. Additionally, we analyze how traditional narratives can be transformed into new forms suitable for digital media, where stories are increasingly told, mediated, and experienced via spaces. Traditional emphases on plot and characters are increasingly shifting toward the significance of ambience and spatial arrangements in digital and interactive media, marking a shift toward what is often called “spatial storytelling.” In the latter half of the semester, we learn about narrative genres. We explore formal patterns and storytelling strategies in each genre and observe the kinds of similar or different patterns that arise in genre-based digital stories. Eventually, we move beyond mere criticism and theoretical understanding by applying our narrative skillsets to creating actual digital content, albeit at a very basic level suitable for those inexperienced with content creation. In the process, students acquire some basic skills to work within new media technologies—especially very basic content creation skills using AI tools.

This course develops a solid understanding of reinforcement learning, a major area within machine learning and artificial intelligence. Reinforcement learning is grounded in various probabilistic and statistical theories and has recently been widely applied to the training of large-scale machine learning models. The course covers the theoretical foundations, applications, and current research trends in this field. Topics include Finite Markov Decision Processes, Dynamic Programming, Temporal Difference Learning, Eligibility Traces, Generalization and Function Approximation, On-policy Approximation of Action Values, Off-policy Approximation of Action Values and Policy Approximation, Meta/Multi-task Learning and RL, and Foundation Model and RL.