For more details on the courses, please refer to the Course Catalog
| Code | Course Title | Credit | Learning Time | Division | Degree | Grade | Note | Language | Availability |
|---|---|---|---|---|---|---|---|---|---|
| ADS5036 | Data Mining | 3 | 6 | Major | Master/Doctor | 1-8 | - | No | |
| This course introduces the concepts, techniques, and applications of data mining. Topics include (1) data mining concepts and methods such as association rule mining, pattern mining, classification, and clustering, and (2) applications of data mining techniques to complex types of data in various fields. Students also learn how to implement data mining algorithms using programming languages. | |||||||||
| ADS5037 | Reinforcement Learning | 3 | 6 | Major | Master/Doctor | - | No | ||
| Reinforcement learning is one powerful paradigm for an enormous range of tasks, including robotics, game playing, consumer modeling and healthcare. In this class, we will provide a solid introduction to the field of reinforcement learning including Markov decision process, planning by dynamic programming, model-free prediction, model-free control, value function approximation, policy gradient methods, integrating learning and planning, exploration and exploitation. | |||||||||
| ADS5038 | Intensive Research | 3 | 6 | Major | Master/Doctor | Korean | Yes | ||
| In this subject, we will learn new concepts and research methods that are discussed in the field of Data Science that is rapidly developing by organizing, analyzing and discussing in detail the latest data related to Data Science. By selecting a professor and conducting research on a specific topic under the guidance, he/she learns experimental skills and approaches for exploring new facts in the process. Results obtained in this process are submitted to the graduation thesis at the end of the semester and the results are evaluated by the graduation thesis review committee. | |||||||||
| ADS5039 | Data and Theories | 3 | 6 | Major | Master/Doctor | Korean | Yes | ||
| This course, Data and Theories discusses various models and theories to understand and apply social science data. Through theoretical frameworks, this course explains people's perceptions and behaviors, and discusses concepts and knowledge to develop a lens or perspective necessary to interpret data generated by social media and social phenomena. By the end of the course, students should be able to get ideas that are helpful for data analysis and understand the principles that have influenced the social adoption or resistance of numerous technologies and innovations that have been produced so far | |||||||||
| AIM5058 | Variational Inference | 3 | 6 | Major | Master/Doctor | 1-4 | Artificial Intelligence | Korean | Yes |
| The goal of inference problem is to find a structure hidden in the data. This can often be achieved by getting the posterior probability distribution which is intractable in many cases. Variational inference (VI) solve this by casting inference problem as an optimization. In this course, we explore VI and stochastic VI after learning basic probability theory and Monte-Carlo methods. The connection between VI and VAE is also provided. | |||||||||
| CHS7001 | Introduction to Blockchain | 3 | 6 | Major | Bachelor/Master/Doctor | Challenge Semester | - | No | |
| This course deals with the basic concept for the overall understanding of the technology called 'blockchain'. We will discuss the purpose of technology and background where blockchain techology has emerged. This course aims to give you the opportunity to think about the limitations and applicability of the technology yourself. You will understand the pros and cons of the two major cryptocurrencies: Bitcoin and Ethereum. In addition, we will discuss the concepts and limitations about consensus algorithm (POW, POS), the scalability of the blockchain, and cryptoeconomics. You will advance your understanding of blockchain technogy through discussions among students about the direction and applicability of the technology. | |||||||||
| 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. | |||||||||
| DAI5001 | Fundamentals of Artificial Intelligence | 3 | 6 | Major | Master/Doctor | 1-8 | Applied Artificial Intelligence | - | No |
| Artificial intelligence is a field of research into information processing models that can mimic human intelligence and cognitive functions. As a fundamental problem of artificial intelligence, it deals with theories and fundamental computational problems on the methods of empirical exploration, reasoning, learning and knowledge expression. It deals with logic-based proof of theorem, game theory, intelligent agent, etc., learns the basic principles of neural network, evolutionary computation, and beigean network, and examines areas such as expert system, computer vision, natural language processing, data mining, information search and bioinformatics as examples of its application. | |||||||||
| DAI5003 | Mathematics for Artificial Intelligence | 3 | 6 | Major | Master/Doctor | 1-8 | Applied Artificial Intelligence | Korean | Yes |
| This subject is a subject that acquires basic mathematics/statistics needed to understand and utilize artificial intelligence for new students entering the artificial intelligence convergence department, and is a statistical foundation for the free use of artificial intelligence in the future. In other words, through this course, students learn the mathematics required to understand machine learning in conjunction with programming. For this purpose, this class will cover essential requirements for machine learning and courses, such as algebra, calculus, linear algebra, and geometry. | |||||||||
| DAI5018 | Advanced Big Data Analysis | 3 | 6 | Major | Master/Doctor | 1-8 | Applied Artificial Intelligence | - | No |
| Recently, as big data processing and visualization have been activated, research on a methodology for efficiently processing big data has been activated. Examples include federated learning, the use of distributed processing platforms such as Hadoop and Spark. This class aims to develop the basics using the R language suitable for big data processing, and then process and visualize big data as the subject of personal research, implement it on a distributed processing platform, and complete personal thesis. | |||||||||
| 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. | |||||||||
| HAI5013 | AI design and human psychology | 3 | 6 | Major | Master/Doctor | 1-4 | Human-Artificial Intelligence Interaction | English | Yes |
| This course discusses how AI should be designed to provide better user experiences. It introduces the basic concepts in Human-AI interaction and Human-computer interaction and provides a deep understanding through seminar and class discussion regarding the psychological effects of AI and user experience from the research in various disciplines including communication, psychology, and computer science. | |||||||||
| HAI5014 | Human AI Interaction | 3 | 6 | Major | Master/Doctor | 1-4 | Human-Artificial Intelligence Interaction | - | No |
| This course explores theoretical foundations of human-AI interaction and students are encouraged to find her own perspectives on the new mode of interactions of people, robots, and AI. | |||||||||
| HAI5017 | HCI and AI practice | 3 | 6 | Major | Master/Doctor | Human-Artificial Intelligence Interaction | - | No | |
| In this class, students will be guided to explore research topics using artificial intelligence and make creative interface design with user experience insights. This class is based on the Human-Center Computing method. At the end of the semester, students will be guided to present their own research topic and results. This class combines lectures and student presentations. | |||||||||
| WIS5021 | Introduction to Informatics | 3 | 6 | Major | Master/Doctor | 1-4 | Interaction Science | - | No |
| The course deals with the nature of Informatics within the information technology space. The core concept of integration of people, technology and information will be addressed. The emphasis will be on the practical dimension of Informatics, real problems, and the socio-economic situations in which they arise. A variety of Informatics tools will be presented from a variety of domains, and their implications for science, engineering, art, the humanities and society will be discussed. | |||||||||