For more details on the courses, please refer to the Course Catalog
| Code | Course Title | Credit | Learning Time | Division | Degree | Grade | Note | Language | Availability |
|---|---|---|---|---|---|---|---|---|---|
| DIM5005 | Realistic Media and Digital Human | 3 | 6 | Major | Master/Doctor | 1-4 | Immersive Media Engineering | Korean | Yes |
| This course introduces a general understanding of digital human technology and knowledge of the latest trends in general. The reason why digital humans are considered a promising field is that they can communicate with users through non-verbal means of communication such as facial expressions by implementing a human-like appearance beyond simple communication through text. In the process of implementing this, artificial intelligence technology is essential for modeling and building interaction technology. Through this course, students can directly conceive and implement various AI processing technologies such as NLP and image deep learning. | |||||||||
| DIM5006 | Immersive media content workshop | 3 | 6 | Major | Master/Doctor | 1-4 | Immersive Media Engineering | Korean | Yes |
| This course provides a broad introduction to create immersive storytelling projects - Learn how to design environments, titles, and interactive elements for VR Storytelling. - Learn how to output VR-Art projects | |||||||||
| DIM5009 | Immersive Media International Standard | 3 | 6 | Major | Master/Doctor | 1-4 | Immersive Media Engineering | - | No |
| This course introduces state-of-the-art international standards and technologies which provide video pre/post-processing, compression, and transmission for immersive media. MPEG immersive video (MIV, ISO/IEC 23090-12), six degrees of freedom (6DoF) video coding standard which is under ISO/IEC SC 29/WG 4 (MPEG Video Coding) is introduced. Video decoding interfaces (VDI, ISO/IEC 23090-13), decoder-level tile/subpicture-based bitstream processing standard which is under ISO/IEC SC 29/WG 3 (MPEG Systems) is also introduced. Recent activities and technologies are explained. Further, practices of using reference SW are also provided. | |||||||||
| DIM5011 | Immersive Media and Computer Vision | 3 | 6 | Major | Master/Doctor | Immersive Media Engineering | Korean | Yes | |
| This course primarily focuses on the fundamental principles and diverse applications of computer vision as used in immersive media. It covers methods for analyzing and interpreting real-world images using computer vision, as well as learning about the latest image generation technologies using generative models, which are garnering significant attention. Additionally, the course offers hands-on experience through case studies and projects, enabling direct practice in applying computer vision in immersive media contexts. | |||||||||
| DIM5012 | Advanced Computer Vision | 3 | 6 | Major | Master/Doctor | Immersive Media Engineering | Korean | Yes | |
| This course thoroughly covers the latest technologies and research topics in the field of computer vision. For enrollment, it requires a basic understanding of Python programming and deep learning, along with essential mathematical knowledge in linear algebra, probability, and statistics. The curriculum includes a range of topics from recognition problems such as Image Classification, Object Detection, Instance/Semantic Segmentation, to generative models like GAN, Diffusion Model, and NeRF. Students will learn about the cutting-edge research in computer vision and ultimately engage in team-based projects aimed at submitting papers to top-tier conferences. | |||||||||
| ESW4008 | Data Science and Security | 3 | 6 | Major | Bachelor/Master | Computer Science and Engineering | - | No | |
| This course is to learn about the AI security and privacy. Additionally, we study the role of AI, data and data analytics for security and privacy applications. This course focuses on applications of AI, machine learning and big data analytics to various security and privacy problems, using various data analysis and AI techniques to solve challenging security and privacy issues. | |||||||||
| ESW5049 | Advanced Topics in Machine Learning | 3 | 6 | Major | Master/Doctor | Computer Science and Engineering | English | Yes | |
| This course is designed for graduate students who have a foundational understanding of machine learning and are looking to delve deeper into advanced concepts and recent ML-based applications. The course focus on recent advances in machine learning and goes in depth on selected topics and methods within machine learning and their applications, including generative models, probabilistic models and Bayesian methods, networks optimization algorithms, multimodal Learning, multitask learning, ensemble learning, attention mechanisms and transformers, interpretable models, graph neural networks, and adversarial learning. By covering these topics, the course also covers applications in domains such as computer vision, natural language processing, and biomedical. By completing this course, the student should be able to critically evaluate machine learning literature and current research trends. The course develops the ability to design, implement, and assess machine learning solutions for real-world applications. | |||||||||
| HAI5002 | User Behavior and Data Analytics | 3 | 6 | Major | Master/Doctor | Human-Artificial Intelligence Interaction | - | No | |
| This course is designed for students who have no previous knowledge of data analytics but wish to acquire these skills in a short period of time. These students will learn how to analyze large data sets and identify patterns that will improve any users' decision-making process. | |||||||||
| HAI5003 | User experience based on big data | 3 | 6 | Major | Master/Doctor | Human-Artificial Intelligence Interaction | - | No | |
| The primary purpose of research related to user experience is to make information and communication technology more useful to humans by improving the interaction of information and communication technology with humans. Existing user experience research has been done through the design of experiments according to the purpose of the research, and through the procedures of data collection, analysis, and statistical reasoning. However, with the proliferation of the Internet, various data became available, and advanced big data analysis technology opens up an opportunity to analyze the user experience without experiments. This class analyzes the user's experiences based on big data and seeks the direction of development of information and communication technology. | |||||||||
| HAI5004 | Linguistic big data and user behavior | 3 | 6 | Major | Master/Doctor | Human-Artificial Intelligence Interaction | - | No | |
| With the advent of the Internet of Things, various sensing technologies have emerged, and all our actions are recorded, transmitted, and stored through SNS, through the smart speaker, and through the smart home. In this class, we analyze human language big data stored through various media, and study the correlation between big data stored online and offline behavior. | |||||||||
| HAI5005 | Social Network Analysis | 3 | 6 | Major | Master/Doctor | Human-Artificial Intelligence Interaction | - | No | |
| This lecture deals with basic methods on social network analysis and its various applications. The basic methods on social network analysis such as graph theory, network analysis, data collection, big data processing will be covered. Also, through a project on analyzing social network big data, students will have practical experience on social network analysis. | |||||||||
| HAI5006 | Computational Social Science | 3 | 6 | Major | Master/Doctor | Human-Artificial Intelligence Interaction | - | No | |
| This course deals with how programming can be applied to modern social science. Python, R, and other relevant programming languages are used for descriptive statistics, inferential statistics, data visualization, machine learning, and deep learning. Multiple examples, quizzes, and projects are conducted in the course. | |||||||||
| HAI5007 | AI-based User Research | 3 | 6 | Major | Master/Doctor | Human-Artificial Intelligence Interaction | - | No | |
| With the development of AI, now it is possible to collect and analyze massive amount of data. In this class, students will learn how to communicate the results of big data with users more effectively and persuasively and how to design UX accordingly. | |||||||||
| HAI5008 | Human-Centered Machine Learning | 3 | 6 | Major | Master/Doctor | Human-Artificial Intelligence Interaction | - | No | |
| In this lecture, students first can learn basic knowledge on machine learning including classification, clustering, and prediction. Also, basic algorithms on machine learning such as SVM, Random Forest, and Neural Networks will be covered. Students then learn how to apply machine learning in human-generated data or human-centered system with diverse case studies. | |||||||||
| HAI5009 | Special Seminar: Interaction Big Data | 3 | 6 | Major | Master/Doctor | Human-Artificial Intelligence Interaction | - | No | |
| This course introduces ‘interaction big data’ and presents how this new field deals with data science, user, and interface in terms of theory and method. Moving from data structure-oriented data science to a new approach of encompassing users and interfaces in data science may lead to enhanced understanding of data-driven thinking. | |||||||||