For more details on the courses, please refer to the Course Catalog
| Code | Course Title | Credit | Learning Time | Division | Degree | Grade | Note | Language | Availability |
|---|---|---|---|---|---|---|---|---|---|
| CHS7004 | Thesis writing in humanities and social sciences using Python | 3 | 6 | Major | Bachelor/Master/Doctor | Challenge Semester | Korean | Yes | |
| This course is to write a thesis in humanities and social science field using Python. This course is for writing thesis using big data for research in the humanities and social sciences. Basically, students will learn how to write a thesis, and implement a program in Python as a research methodology for thesis. Students will learn how to write thesis using Python, which is the most suitable for processing humanities and social science related materials among programming languages and has excellent data visualization. Basic research methodology for thesis writing will be covered first as theoretical lectures. Methodology for selection of topics will be discussed also. Once a topic is selected, a lecture on how to organize related research will be conducted. In the next step, students learn how to write necessary content according to the research methodology. Then how to suggest further discussion along with how to organize bibliography to complete a theoretical approach. The basic Python grammar is covered for data analysis using Python, and the process for input data processing is conducted. After learning how to install and use the required Python package in each research field, the actual data processing will be practiced. To prepare for the joint research, learn how to use the jupyter notebook as the basic environment. Learn how to use matplolib for data visualization and how to use pandas for big data processing. | |||||||||
| CHS7006 | A new human AI Sapiens Experience Design | 3 | 6 | Major | Bachelor/Master/Doctor | Challenge Semester | Korean | Yes | |
| This course analyzes the impact of artificial intelligence (AI), big data, and digital platforms on consumer behavior and market ecosystems in the rapidly changing digital environment. Building upon this analysis, it explores experience design principles suited for the new human era, ‘AI Sapiens’. Focusing on human-AI interaction, it investigates user experience (UX) and service design strategies to help businesses and society adapt. It examines how AI influences consumer psychology and behavior, studies AI-driven market shifts, and analyzes digital transformation cases. The course also covers AI/data-driven UX/UI design concepts and applications of chatbots, voice recognition, and recommendation systems. It explores related technologies such as 5G, IoT, autonomous vehicles, and smart factories, while addressing ethics, privacy, and human-centered design. Through hands-on exercises and project-based learning, students design AI-based services, analyze real-world cases, and propose experience design solutions. This cultivates creative problem-solving skills and prepares students to become AI experience design experts who meet business and societal needs in the digital transformation era. | |||||||||
| CHS7007 | AI-Based Media Text Comprehension | 3 | 6 | Major | Bachelor/Master/Doctor | Challenge Semester | Korean | Yes | |
| This course aims to equip students with the ability to critically analyze and understand various forms of media texts—such as news, advertisements, films, and social media—through the use of artificial intelligence (AI). Students will learn techniques in natural language processing (NLP), including sentiment analysis, keyword extraction, and text summarization, as well as methods for analyzing visual content using AI models like CNNs and GANs. The course also addresses issues of trustworthiness and ethical concerns related to AI-generated content. Combining theoretical instruction with practical application, students will complete hands-on assignments and projects using Python-based AI tools such as GPT and Gemini AI. | |||||||||
| CHS7008 | Strategic Decision-Making with AI | 3 | 6 | Major | Bachelor/Master/Doctor | Challenge Semester | Korean | Yes | |
| This course equips students with the essential skills to make strategic decisions using generative AI. As technology rapidly transforms every industry, the ability to critically evaluate AI-generated information and integrate it into decision-making has become a core competency. This course addresses that need by preparing students to work with AI not just as a tool, but as a collaborative partner. Students will gain a foundational understanding of AI systems, particularly the workings of generative AI models. They will learn to analyze unstructured outputs such as text and predictions, assess data reliability, and identify biases. Ethical and responsible use of AI is emphasized to build both technical and social awareness. Going beyond traditional data analysis, the course explores human-AI collaboration in solving real-world problems. Students will examine decision-making case studies across industries including business, healthcare, finance, and policy. Through team-based projects, they will apply AI tools to complex strategic challenges. The curriculum reflects global academic standards, drawing inspiration from courses at MIT, Stanford, and Carnegie Mellon. By aligning with leading institutions, the course helps students build globally competitive capabilities. Ultimately, this course develops future-ready professionals who combine AI fluency with critical thinking and leadership. It is an essential foundation for navigating a world where AI shapes strategy. | |||||||||
| CLA7101 | AI-Assisted Research Methods for Linguistic Data | 1 | 2 | Major | Bachelor/Master/Doctor | Liberal Art | Korean | Yes | |
| AI-Assisted Research Methods for Linguistic Data is an interdisciplinary course designed for undergraduate and graduate students who wish to conduct research using linguistic data. Through step-by-step, hands-on activities, students engage in the entire research process—from identifying a research topic to collecting and preprocessing data, conducting experiments, visualizing results, and writing an academic paper—with the support of generative AI and a variety of AI-assisted tools. Upon completing the course, students will be able not only to independently carry out the entire process of linguistic data research but also to effectively select and utilize appropriate AI tools at each stage. They will also develop the ability to critically evaluate AI-generated outputs and apply them to their research in an informed, responsible, and independent manner. | |||||||||
| COG5034 | Understanding of software design | 3 | 6 | Major | Master/Doctor | Computer Science Education | - | No | |
| In this lecture, we learn advanced programming techniques using object-oriented and generalized programming languages so that they can be applied to practical and educational sites. Understand and utilize the basic concepts of object-oriented language, objects, classes, polymorphisms, inheritance, etc., and cultivate the ability to solve problems using object-oriented language. We also learn how to design software that operates in various environments through generalized programming techniques. | |||||||||
| 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. | |||||||||
| DAI5004 | Advanced Machine Learning and Deep Learning | 3 | 6 | Major | Master/Doctor | 1-8 | Applied Artificial Intelligence | Korean | Yes |
| In this course, you will learn about machine learning, deep learning, and associated optimization techniques, as well as basic neural networks. And the core models of video processing and natural language processing learn about the theory, application and practice of CNN and RNN. | |||||||||
| DAI5013 | Advanced Computer Vision | 3 | 6 | Major | Master/Doctor | 1-8 | Applied Artificial Intelligence | English | Yes |
| Computer vision is one of the fastest developing fields of artificial intelligence in recent years, and its purpose is to acquire, process, analyze, and understand starting information such as photographs and videos recording the three-dimensional world. In this course, students learn basic concepts and methodologies related to undergraduate computer vision and their applications. Topics covered in this course include image processing and segmentation, feature point detection, optics, image tracking, camera model, 3D reconstruction, and recognition and detection of people and objects. | |||||||||
| DAI5015 | Theories of Convergence Information | 3 | 6 | Major | Master/Doctor | 1-4 | Applied Artificial Intelligence | - | No |
| To study deep into digital content, an accurate understanding of “Digital Information” which is the basic unit of digital content is necessary. This course is an introductory course on digital information. This course will introduce various characteristics of digital information, and investigate it in terms of pricing information, digital intellectual property, and so on. | |||||||||
| DAI5017 | Data Science Computing | 3 | 6 | Major | Master/Doctor | 1-8 | Applied Artificial Intelligence | Korean | Yes |
| For graduate students who are not majored in data science, artificial intelligence, this course provides the basic concepts of Python, a programming language for utilizing data science and artificial intelligence, and foster the ability to carry out package and visualization for utilizing artificial intelligence and big data, as well as actual data analysis and applications. | |||||||||
| DAI5022 | Entertainment User Experience | 3 | 6 | Major | Master/Doctor | Applied Artificial Intelligence | Korean | Yes | |
| We address user experience in entertainment services, including games/XR, as well as traditional ICT products. Specifically, it performs learning from a user experience and data privacy perspective within entertainment services. | |||||||||
| DAI5024 | Advanced Research Writing: From Ideation to Publication | 3 | 6 | Major | Master/Doctor | Applied Artificial Intelligence | Korean | Yes | |
| This comprehensive hybrid course is designed for master’s and PhD students seeking to elevate their technical research writing skills. This course will help students successfully navigate through the publication process. Tailored to the field of Computer Science, this 16-week program covers advanced academic writing, paper crafting, journal selection, submission, revision, presentation skills, and post-acceptance procedure. | |||||||||
| DAI5025 | Multimodal Learning | 3 | 6 | Major | Master/Doctor | 1-4 | Applied Artificial Intelligence | - | No |
| Multimodal learning is a field that deals with techniques for integrating and learning from various modalities (images, text, audio, etc.). This course introduces the fundamental concepts and latest research in multimodal learning, aiming to develop students' ability to effectively analyze and utilize multimodal data. Through this course, students will learn multimodal data processing techniques, multimodal learning methodologies, and methods for designing multimodal models. | |||||||||
| DAI5027 | Advanced Generative AI | 3 | 6 | Major | Master/Doctor | 1-8 | Applied Artificial Intelligence | - | No |
| Advanced Generative AI explores the characteristics and limitations of large language models (LLMs) and the process of designing, implementing, and evaluating reliable AI applications by integrating external knowledge and tools. Key topics include prompt engineering; application development with LangChain; state, memory, and workflow design with LangGraph; retrieval-augmented generation (RAG) using embeddings and vector stores; intelligent agents capable of tool use and planning; and multi-agent collaboration systems. Students apply these techniques to tasks such as code generation and data analysis, developing the ability to select and implement system architectures suited to specific problems. The course integrates conceptual learning, progressive hands-on exercises, and project-based learning. Students complete the development process from problem definition and data and knowledge organization to model–tool integration, system implementation, performance evaluation, and iterative improvement. They also assess answer accuracy, the appropriateness of supporting evidence, task success rates, response times, and costs. Through testing, deployment, and execution-log analysis, students learn quality management practices essential for operating real-world services. The course also examines hallucinations, bias, privacy concerns, and risks associated with tool use, helping students identify situations that require human review and intervention. | |||||||||