For more details on the courses, please refer to the Course Catalog
| Code | Course Title | Credit | Learning Time | Division | Degree | Grade | Note | Language | Availability |
|---|---|---|---|---|---|---|---|---|---|
| DAI5019 | Graph Mining and Learning | 3 | 6 | Major | Master/Doctor | Applied Artificial Intelligence | - | No | |
| Graph is a structure that can represent diverse relations. Social networks, World Wide Web (WWW), power grids, etc., can be represented as graphs. In this lecture, we will first learn how to analyze graphs with various applications. Also, we will learn about graph machine learning that learns graph representations. We will discuss recent applications on graph learning. | |||||||||
| DBA5109 | Special Topics in Industry and Culture | 3 | 6 | Major | Master/Doctor | Business Administration | English | Yes | |
| Robots and artificial intelligence (AI) are not tethered to labs and factories anymore and becoming commonplace in many facets of everyday lives. This seminar discusses the changes that robotics and AI will bring to corporate management and work environments from industry and cultural perspectives. First, the managerial and socio-technical implications of robots and AI will be discussed by understanding the current trends in such technologies becoming teammates rather than tools in the workplaces. In doing so, changes in several aspects of work will be discussed, such as teamwork, work process, decision-making, creativity, and leadership. Second, the course will examine how robots and AI transform the relationships between customers and companies from cultural standpoints. This will review and analyze robots and AI depicted and discoursed in commercials and mass media. | |||||||||
| DHC7004 | Program Planning for Health Behavior Change in Digital Health Era | 3 | 6 | Major | Bachelor/Master/Doctor | 1-8 | Digital Health | Korean | Yes |
| Provides an overview of the breadth of programs and diversity of settings in the field of healtheducation in healthpromotion, and an opportunity to develop skills in programplanning in digital health era. Explains the importance of health behavior as a contributor to current publichealthproblems and the role of health education and healthpromotion programs in addressing them, drawing examples from the literature on community-based health education, patient education, school health, and work-sitehealthpromotion. Also discusses issues of ethical standards and quality assurance in healtheducation and health promotion. Upon successfully completing this course, students will be able to (1) explain the importance of health behavior as a contributor to current public health problems, (2)describe the elements of at least two behavior change theories and their applicability to developing healtheducation/health promotion programs, (3) describe at least three intervention methods and their applicability to successful healtheducation/healthpromotion programs, and (4) demonstrate skills in planning a healthbehavior change program by successfully completing a written needs assessment. | |||||||||
| DIM5002 | Metaverse Bigdata Analytics | 3 | 6 | Major | Master/Doctor | Immersive Media Engineering | English | Yes | |
| This course deals with collection, analysis, prediction, and implication retrieval from diverse metaverse data. Basics in data analytics, examining extant studies on metaverse analysis, designing a new study could be included for this course. | |||||||||
| ECE5947 | HCI Design | 3 | 6 | Major | Master/Doctor | 1-4 | Electrical and Computer Engineering | Korean | Yes |
| HCI, the general concept defining the interaction between the user and the product, suggests that the initiative of product development is shifted from the knowledge of the developer to the satisfaction of the user. Consequently the product should be designed such that it is more appealing to diverse user groups and its use is easy to learn. This lecture provides students with principles and techniques of HCI necessary to design HW and SW component of the product to the user’s satisfaction. | |||||||||
| 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. | |||||||||
| ERP4001 | Creative Group Study | 3 | 6 | Major | Bachelor/Master | - | No | ||
| This course cultivates and supports research partnerships between our undergraduates and faculty. It offers the chance to work on cutting edge research—whether you join established research projects or pursue your own ideas. Undergraduates participate in each phase of standard research activity: developing research plans, writing proposals, conducting research, analyzing data and presenting research results in oral and written form. Projects can last for an entire semester, and many continue for a year or more. SKKU students use their CGS(Creative Group Study) experiences to become familiar with the faculty, learn about potential majors, and investigate areas of interest. They gain practical skills and knowledge they eventually apply to careers after graduation or as graduate students. | |||||||||
| ESW4025 | Artificial Intelligence Ethics | 3 | 6 | Major | Bachelor/Master | Computer Science and Engineering | English | Yes | |
| With the current development of artificial intelligence, we can meet them in various parts of society. However, artificial intelligence is an amoral that cannot make ethical judgments on its own. Therefore, we need to understand and solve various ethical problems caused by artificial intelligence. In this class, we will look at the ethical problems, causes, and solutions of artificial intelligence. First, we will briefly learn about artificial intelligence and then look at the ethical problems they have. They can be largely divided into data, algorithms, and applications, so we will look at each of them. We will then look at the causes of these problems. In addition, we will look at algorithms and examples that actually solve them based on the analyzed problems and causes. | |||||||||
| ESW4026 | Computer Networks and Artificial Intelligence | 3 | 6 | Major | Bachelor/Master | Computer Science and Engineering | - | No | |
| This course aims at the improvement of the capability to apply Artificial Intelligence (AI) technology to the Internet where AI is one of core technologies in the 4th industrial revolution. For this aim, it explains the fundamental protocols and systems of computer networks and security, and the AI technology which can be applied to the computer networking technology. To prepare for 6G network era, it deals with Intent-Based Networking (IBN), Cloud-based Security Services, Intelligent Internet of Things, and Wireless Networking for autonomous vehicles. Especially, AI-based networking and security technologies are handled for 6G core networks. Also, this course teaches students the protocol layers such as data link layer, network layer, transport layer, and application layer. The contents of this course consist of introduction for 1 week, 6G network technologies for 5 weeks, and computer network layers for 8 weeks. As the expected benefits of this course, the students not only learn the foundational knowledge of computer networks and security, but also, for the 4th industrial revolution era, they can be educated up to SW-AI experts who can graft the technologies of the AI and Machine Learning (ML) to the computer networking field. | |||||||||
| ESW5025 | Infra Networks and Security | 3 | 6 | Major | Master/Doctor | Computer Science and Engineering | - | No | |
| This course introduces technologies and standards of infrastructure networks and security. This course explains the technologies of networks and applications in the network infrastructure (e.g., cloud, Internet of Things, and vehicular networks), and also the technologies of security and privacy in this infrastructure. The contents of this course are as follows. - Week 1: Introduction to Infra Networks and Security - Week 2: Internet and Computer Networks - Week 3: Software-Defined Networking (SDN) - Week 4: Network Functions Virtualization (NFV) - Week 5: OpenStack-Based Cloud Systems - Week 6: Cloud-Based Security Service Systems - Week 7: YANG-Based Data Modeling - Week 8: Term Project Proposal - Week 9: NETCONF-Based Network Management - Week 10: Internet-of-Things (IoT) Networking - Week 11: IoT Applications - Week 12: IoT Security - Week 13: Vehicular Networking - Week 14: Vehicular Network Applications and Security - Week 15: Term Project Presentation This course will be delivered in the format of a flipped class. It has four homeworks and one term project. | |||||||||
| ESW5041 | Digital Health Security | 3 | 6 | Major | Master/Doctor | 1-3 | Computer Science and Engineering | - | No |
| Digital THerapeutics sis one of the rapidly growing field which convergies ICT and Medical technologies. In this seminar, The cutting edge technologies such as IoT, AI, Display, will be presented and discussed. | |||||||||
| 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. | |||||||||