Yonsei University

In this studio course, students acquire knowledge and hands-on experience at the intersection of cityscapes, artificial intelligence, and data-driven site analysis and urban design development methods. The lectures focus on the theoretical background of data metrics relevant to urban design. The hands-on sessions help students gain technical skills needed to apply the knowledge. Students apply the theories and skills to analyze the study site, extract current issues, and develop concepts and directions for design interventions. The goal is to acquire foundational knowledge on the theoretical basis, measurement methods, and limitations of key quantitative indicators related to urban design, and learn how to select indicators that align with the objectives of a design project. Students acquire skills in data acquisition and preprocessing; learn how to generate, post-process, and analyze indicators using various spatial data and artificial intelligence; and develop effective communication methods through visualization. Students also learn the process of applying data analysis techniques to the design site to assess current conditions, identify issues and challenges, and establish a foundation for developing planning concepts and alternatives.

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. 

This course covers the essence of quantum computing and various quantum machine learning techniques. Quantum computing has the potential to outperform classical computing and to solve problems that were believed to be intractable otherwise. With rapid advances in quantum technology, current technology is expected to be disrupted in many ways. Quantum computing opens up tremendous opportunities for data science in the big data era where computational power is of critical importance.  

This course equips students with theoretical backgrounds to be able to apply the principles of quantum computing in solving various challenges of modern data science problems. Topics include Introduction to quantum data science & quantum machine learning, Machine learning basics & classical information, Quantum mechanics & quantum information, Circuit model of quantum computation & reversible computing, Black-box model of computation & related quantum algorithms, Quantum phase estimation & Quantum Fourier transform, Unstructured search & quantum amplitude estimation, Quantum linear systems solver & quantum support vector machine, Quantum kernel method, and Quantum neural network. 

Prerequisites: Linear algebra, calculus, probability theory and statistics, Quantum mechanics, Python or Matlab (or similar programming skills) 

This course offers a survey of modern Irish literature, from the Irish Dramatic Revival with the founding of the Abbey Theatre in 1904 to the late twentieth century. Irish authors are often studied as part of English literature, but Ireland has its own unique cultural history. While the focus of the course is drama, it also covers some poems and short stories. The course enhances students' understanding of Irish culture and history: Celtic mythology, Irish landscape, fairies and folklore, Catholicism and the Protestant ascendancy, British colonialism, independence, and the Celtic Tiger. We read representative work by major Irish authors, including W. B. Yeats, Lady Augusta Gregory, J. M. Synge, Sean O`Casey, James Joyce, Samuel Beckett, Brian Friel, Seamus Heaney, and Marina Carr. We explore how these authors respond to the idea of Irishness, as their works show persistent interest in Irish history and Irish identity. 

This course covers the fundamental concepts of databases—an essential component in implementing e-business information systems—including the entity-relationship model, relational databases, and the use of structured query language (SQL). Through individual projects, students also explore how to integrate databases with business information systems. Topics include Introduction to Database Industrial Information Management, Introduction to Structured Query Language (SQL), Relational model and normalization, Database design using normalization, Data modelling with the entity-relationship model, Transforming data models into a database design, SQL for database construction and application processing, Database redesign, Managing multi-user databases, Web Server Environment, and Data warehouses, business intelligent systems, and big data. 

This course introduces computer vision with a focus on modern deep learning. We start with the foundational concepts and history of the field. We then dive into the key architectures that have shaped modern computer vision. We study convolutional neural networks (CNNs) and Vision Transformers (ViT), learning how they work and how they are used for fundamental tasks like image classification, object detection, and semantic segmentation. Then, we cover 3D computer vision, including problems like 3D reconstruction. Finally, students focus on deep generative models for vision, exploring how they are used to create realistic images and videos.  

Prior to taking this course, it is recommended that students take courses in linear algebra and probability and statistics.  

Topics include Introduction to Computer Vision; Basics of Digital Images and Processing; Machine learning and neural networks; Convolutional neural networks (CNNs); Computer vision problems; Vision transformers (ViTs) for computer vision; 3D Computer Vision; Generative Models: VAEs, GANs; and Generative Models: Diffusion Models, Multimodal models.