For more details on the courses, please refer to the Course Catalog
| Code | Course Title | Credit | Learning Time | Division | Degree | Grade | Note | Language | Availability |
|---|---|---|---|---|---|---|---|---|---|
| ADD4011 | Contemporary Architecture 1 | 3 | 6 | Major | Bachelor/Master | 1-8 | Architecture | English | Yes |
| Understand Historical, Theoretical, and Critical Background of the Contemporary Architecture(CA) by Analyzing Projects and Writings by Pritzker Prize (Regarded as a Noble Prize in Architecture Discipline) awarded Architects. Course Goal : Ability to articulate contemporary projects, architects, and styles based on global/ local issues, universal/ personal values, and historical/ technological agendas. 1. Understand Material Innovation in CA 2. Understand Neo-Modernist & Post-Modernist trend in CA 3. Understand Critical Regionalism in CA 4. Understand Issues of Sustainability in CA 5. Programs and Diagrams in CA 6. Structural Expressions and Digital Measures in CA 7. Theory / Practice / Role of Pritzker Recipients in CA | |||||||||
| ADD4013 | Contemporary Architecture 2 | 3 | 6 | Major | Bachelor/Master | 1-8 | Architecture | English | Yes |
| This course is in English. The course covers major east-coast, mid-west, and west-coast cities in the USA Throughout the semester, the course will cover architecture and urbanism of Boston, New York, Chicago, and Seattle. The course will cover architectural and urban narratives and their physical manifestations between 1870~2020. Following are the seven key questions that the course will raise. 1. What causes the initiation of Contemporary Korea American City (CKAC)? 2. What is the role of Landmark Architecture during the growth of CKAC? 3. What is the role of Streets and Squares in CKAC? 4. What is the urban and Architectural Regeneration strategy of CKAC? 5. How does CKAC Record Time and Urban Narrative architecturally? 6. What is the role of Public Architecture in CKAC? 7. How does the Campus Architecture contribute to CKAC? | |||||||||
| ADD5022 | Advanced Studieson on Urban Planning | 3 | 6 | Major | Master/Doctor | 1-4 | Architecture | - | No |
| The course introduces a professional planning method of urban space with proper case studies. It especially considers developing the skill of data analysis, programming and implementation methods in urban planning and design. | |||||||||
| ADD5026 | Urban Design | 3 | 6 | Major | Master/Doctor | 1-4 | Architecture | Korean | Yes |
| According to the given planning program, the course leads to design practice which requires to systemize a spatial form of city or specific region. During the desugn process, the functional solution of urban space will be critically discussed on the basis of legal requirements and other importanct planning matters. | |||||||||
| CHS7002 | Machine Learning and Deep Learning | 3 | 6 | Major | Bachelor/Master/Doctor | Challenge Semester | - | No | |
| This course covers the basic machine learning algorithms and practices. The algorithms in the lectures include linear classification, linear regression, decision trees, support vector machines, multilayer perceptrons, and convolutional neural networks, and related python pratices are also provided. It is expected for students to have basic knowledge on calculus, linear algebra, probability and statistics, and python literacy. | |||||||||
| COV7001 | Academic Writing and Research Ethics 1 | 1 | 2 | Major | Master/Doctor | SKKU Institute for Convergence | Korean | Yes | |
| 1) Learn the basic structure of academic paper writing, and obtain the ability to compose academic paper writing. 2) Learn the skills to express scientific data in English and to be able to sumit research paper in the international journals. 3) Learn research ethics in conducting science and writing academic papers. | |||||||||
| COV7006 | Academic Writing in the AI Era | 1 | 2 | Major | Master/Doctor | SKKU Institute for Convergence | Korean | Yes | |
| This 1-credit online graduate course guides dissertation-track students to responsibly utilize generative AI. Developed under the BIGs 2.0 initiative ("Graduate Common Curriculum Development"), it inherits the prerequisite structure for dissertation defense while integrating AI practical applications with research ethics. Based on the Generative AI Research Ethics Guidelines for University Researchers (MOE, NRF, KCURE, June 2026), the 16-week curriculum covers the entire research lifecycle across four monthly modules: -Month 1: AI fundamentals, limitations, and core research ethics (FFP & authorship). -Month 2: AI application principles in literature search, topic formulation, and data analysis. -Month 3: Academic writing combining drafting, refinement, and cross-validation. -Month 4: AI usage disclosure, copyright issues, and final verification responsibility. Research ethics forms a key pillar, occupying roughly one-third of the term. By focusing on core principles, the course builds adaptive resilience to evolving tools and proactively prevents misconducts like fake citations or unauthorized manuscript uploads. Ultimately, as part of the core graduate curriculum, it aims to foster researchers who use AI not just proficiently, but responsibly. | |||||||||
| COV7007 | Academic Paper Writing in the Age of AI | 1 | 2 | Major | Master/Doctor | SKKU Institute for Convergence | - | No | |
| This course was developed for graduate students preparing manuscripts for scientific journals within an academic writing environment increasingly shaped by generative AI. Its foundational premise is that while AI can assist with linguistic expression (English drafting), it cannot replace logical reasoning, critical decision-making, and academic accountability. As language barriers diminish, a researcher's primary competitive advantage has shifted from superficial expression to rigorous logic and judgment. This course aims to systematically train students in the core competencies required during this paradigm shift. | |||||||||
| ECA4702 | Advanced Foundation EngineeringⅠ | 3 | 6 | Major | Bachelor/Master |
3-4
1-4 |
Civil, Architectural and Environmental System Engineering | English | Yes |
| Geotechnical aspects of the analysis, design, and construction of foundation systems and earth retaining structures: Analysis and design of shallow and Deep foundations including piles and caissons, Analysis and design of earth retaining structures including gravity and cantilever retaining walls, sheet pile walls and other systems, Analysis and design of Braced excavation, Analysis and design of reinforced earth structure. | |||||||||
| ECA5501 | Building Energy Management | 3 | 6 | Major | Master/Doctor | 1-4 | Civil, Architectural and Environmental System Engineering | Korean | Yes |
| Building are designed and built to meet the needs of people and their activities. Resources to meet the needs include the building itself, the energy used, and skills at hand to operate the building. The objective of building energy management is to minimize the energy used for operating the building while maintaining comfort conditions for the occupants. In this course, basic concept and the process of building energy management will be introduced. And the energy management trchniques will be reviewed in various steps such as energy audit, idenyifying opportunities, evaluation, retrofit and followthrough. | |||||||||
| ECA5962 | Global Construction Economy | 3 | 6 | Major | Master/Doctor | 1-8 | Civil, Architectural and Environmental System Engineering | English | Yes |
| Global economy in construction industries changes very fast and affects on the success of mega project. Characteristics of global construction industries and economy are covered for the success of the construction projects. Basic theories of economy, housing and hedging on construction purchase. | |||||||||
| ERC5001 | Global Collaboration Research | 3 | 6 | Major | Master/Doctor | Engineering | Korean | Yes | |
| This class aims to enhance global competence as a researcher who study innovative growth fields through overseas dispatch of graduate students. Student’s who are dispatched overseas and carrying out joint research in world university will learn - to establish global network for future research - to strive for the creation of new industries in the field of sustainable development and innovative growth of mankind - to grow into global innovative leader with the competence of ‘Value creation’, ‘Convergence’, ‘Innovation’, and ‘Collaboration’ through innovation in creative convergence experiences that which cross the intercultural, interdisciplinary and intergeneration. In this class, students are expected to focus on projects during the overseas dispatch period to improve the quality of their research. Each researcher will present final report after they complete dispatch study and will be given their credit based on the presentation. | |||||||||
| ERC5002 | Interdisciplinary research in global collaboration 1 | 3 | 6 | Major | Master/Doctor | 1-8 | Engineering | - | No |
| This class is aiming to foster graduate students as a global researcher by pursuing top-tier interdisciplinary research with global collaborators. In addition, the students will improve their own global competence to become a global innovative leader by establishing global academic networks. | |||||||||
| ERC5003 | Interdisciplinary research in global collaboration 2 | 3 | 6 | Major | Master/Doctor | 1-8 | Engineering | - | No |
| This class is aiming to foster graduate students as a global researcher by pursuing top-tier interdisciplinary research with global collaborators. In addition, the students will improve their own global competence to become a global innovative leader by establishing global academic networks. | |||||||||
| ERC5005 | Machine Learning Essentials for Engineers | 3 | 6 | Major | Master/Doctor | Engineering | - | No | |
| As interest in artificial intelligence and machine learning grows, this course is designed to offer mathematical insights into machine learning techniques other than deep learning, enabling students to comprehend and effectively apply them. The course covers a range of topics including Imbalanced Learning, Bayesian Neural Networks, Monotonic Neural Networks, Neural Additive Models, Gaussian Process, Ensemble Learning, Expectation and Maximization, Neighbor Embedding, and more. This will facilitate students' understanding of machine learning-related research papers and equip them to solve real-world engineering problems in their respective fields of study. | |||||||||