For more details on the courses, please refer to the Course Catalog
| Code | Course Title | Credit | Learning Time | Division | Degree | Grade | Note | Language | Availability |
|---|---|---|---|---|---|---|---|---|---|
| CHS5004 | Internship in Foreign Trade | 3 | 6 | Major | Master/Doctor | Challenge Semester | Korean | Yes | |
| This course is offered to master program students in the Department of Foreign Trade at the graduate school, for 3 credits after performing an internship in a trade-related company using the summer challenge semester. The head of the department grants credits when an activity report is submitted after internships of at least 60 days or 300 hours with an evaluation report of the company. Students, participating on this program, are expected to utilize their theoretical knowledge which they have learned in school about trade management activities and use the internship opportunity as a course to prepare for entry into the real world. After a student recruits an internship company, the student applies and registers for this internship course to the department, while the school may provide internship information when available or possible. Any internship-related revenue, income expenses, and safety management belong to the intern student and/or the company concerned, and the university authorities have a secondary duty of care for the students. To share information, the internship report will be posted on the department's website. (Personal information removed) | |||||||||
| 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. | |||||||||
| CHS7003 | Artificial Intelligence Application | 3 | 6 | Major | Bachelor/Master/Doctor | Challenge Semester | - | No | |
| Cs231n, an open course at Stanford University, is one of the most popular open courses on image recognition and deep learning. This class uses the MOOC content which is cs231n of Stanford University with a flipped class way. This class requires basic undergraduate knowledge of mathematics (linear algebra, calculus, probability/statistics) and basic Python-based coding skills. The specific progress and activities of the class are as follows. 1) Listening to On-line Lectures (led by learners) 2) On-line lecture (English) Organize individual notes about what you listen to 3) On-line lecture (English) QnA discussion about what was listened to (learned by the learner) 4) QnA-based Instructor-led Off-line Lecture (Korean) Lecturer 5) Team Supplementary Presentation (Learner-led) For each topic, learn using the above mentioned steps from 1) to 5). The grades are absolute based on each activity, assignment, midterm exam and final project. Class contents are as follows. - Introduction Image Classification Loss Function & Optimization (Assignment # 1) - Introduction to Neural Networks - Convolutional Neural Networks (Assignment # 2) - Training Neural Networks - Deep Learning Hardware and Software - CNN Architectures-Recurrent Neural Networks (Assignment # 3) - Detection and Segmentation - Generative Models - Visualizing and Understanding - Deep Reinforcement Learning - Final Project. This class will cover the deep learning method related to image recognitio | |||||||||
| 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. | |||||||||
| 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. | |||||||||
| DBA5080 | Statistics for Business | 3 | 6 | Major | Master/Doctor | 1-4 | Business Administration | Korean | Yes |
| Formulas are not the everyday language of most students. Statistics should be presented in a way that managers learn best. This class presumes no background in Calculus and minimizes the use of mathematical languages. Instead we use extended examples through the class. This class covers probability distribution, sampling distribution, confidence interval, hypothesis testing. We also cover the analysis of variance and regression, using EXCEL, SPSS or SAS. | |||||||||
| ECO4001 | Macroeconomics Ι | 3 | 6 | Major | Bachelor/Master |
3-4
1-4 |
Economics | Korean,English | Yes |
| Based on both static and dynamic analysis of macroeconomics, this course covers research-oriented contents of macroeconomic models, monetary economy, micro foundations of private sectors, macroeconomic policy, and new macroeconomic theory developed recently. | |||||||||
| ECO4002 | Microeconomics Ι | 3 | 6 | Major | Bachelor/Master |
3-4
1-4 |
Economics | English | Yes |
| The course is on the basic price theory. It strongly emphasizes both formulation and solving of mathematical models and development of economic intuitions. The topics include consumer and producer behavior, theory of competitive and noncompetitive markets, and welfare economics. The course will cover both partial and general equilibrium models. | |||||||||
| ECO4006 | Econometrics Ι | 3 | 6 | Major | Bachelor/Master | 1-4 | Economics | English | Yes |
| This course is intended to provide a basic knowledge of econometric theory relevant for carrying out empirical works in economics. Course starts by reviewing some concepts of linear algebra, probability theories, and statistical theories. It proceeds to classical linear model and its asymptotic theory, inferences. Estimation and testing methods based on least squares, weighted least squares, maximum likelihood, instrumental variables will be discussed. | |||||||||
| ECO4008 | Data Science for International Trade | 3 | 6 | Major | Bachelor/Master | Economics | Korean | Yes | |
| This course guides students through the process of writing an empirical research report on a self-selected topic in international trade. The class introduces key themes in the field, such as how events shape trade flows, the characteristics of exporting and multinational firms, and the effects of shifts in the trade environment on domestic economic agents, including workers and firms. With support from the instructor and AI tools, students develop a specific research question and conduct an empirical analysis. The course emphasizes hands-on empirical work. Students learn how to gather and preprocess micro-data, perform descriptive statistical analysis, and apply core micro-econometric methods using Stata. AI tools are used throughout the course to support data exploration, coding, visualization, and interpretation of results, helping students integrate AI effectively into their empirical investigation. | |||||||||
| ECO5015 | Econometrics Seminar Ι | 3 | 6 | Major | Master/Doctor | 1-4 | Economics | - | No |
| This course is basically designed for those who plan to write a dissertation on econometrics. Students will be guided with instructions on how to survey the related literature, how to build up an alternative model to alleviate problems or limitations of the existing models, and how to prove main arguments empirically. | |||||||||