For more details on the courses, please refer to the Course Catalog
| Code | Course Title | Credit | Learning Time | Division | Degree | Grade | Note | Language | Availability |
|---|---|---|---|---|---|---|---|---|---|
| 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. | |||||||||
| 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. | |||||||||
| CON4013 | Artificial Intelligence Data Analytics | 3 | 6 | Major | Bachelor/Master | Consumer Science | Korean | Yes | |
| This course covers advanced data analysis methodologies using modern artificial intelligence techniques, including machine learning and deep learning. Students will develop the ability to perform in-depth processing and analysis of data in various forms and scales, and derive meaningful insights. Through this course, students will acquire sophisticated analytical capabilities applicable to various research fields, including consumer studies, and cultivate problem-solving skills to propose solutions for real-world problems through hands-on programming exercises. | |||||||||
| CON4014 | Data Science for Causal Inference | 3 | 6 | Major | Bachelor/Master | Consumer Science | Korean | Yes | |
| This course covers advanced data analysis techniques for evaluating the causal effects of interventions designed to influence consumer behavior. Topics include the potential outcomes framework, causal analysis methods, model estimation and validation using data analysis tools, and real-world applications through replication studies. Students will gain an understanding of the causal inference in data-driven decision making and develop the skills to apply these concepts. | |||||||||
| CON4015 | Data-Based Quantitative Research Methods | 3 | 3 | Major | Bachelor/Master | Consumer Science | English | Yes | |
| Data-Based Quantitative Research Methods is a methodological course that introduces the core principles of quantitative research and the procedures of data-driven empirical analysis used across the social sciences. The course is designed to help students understand the full process through which quantitative research formulates research questions, organizes and analyzes data, interprets statistical results, and ultimately derives evidence-based conclusions. Students will learn essential concepts in quantitative inquiry, including research design, variable measurement, sampling strategies, and assessments of validity and reliability. Through hands-on work with Stata, they will conduct key stages of empirical analysis such as data cleaning, descriptive statistics, exploratory data analysis, and regression modeling. This practical engagement will enhance their applied research skills. By the end of the course, students will be able to recognize data structures and patterns, interpret analytical outputs, and use empirical evidence to explain social phenomena. The course focuses on building a strong conceptual understanding of quantitative research and developing students’ capacity to apply data effectively across a wide range of social science research contexts. | |||||||||
| 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. | |||||||||
| 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. | |||||||||
| FSE5003 | Social Economy & Social Entrepreneurship | 3 | 6 | Major | Master/Doctor | 1-4 | Social Entrepreneurship and Humanistic Future Studies | Korean | Yes |
| The main area of activity of the social entrepreneur is the social economy. Social economy is one of organizing forms and ways of economic activities in comtemporary societies but it has received the least systematic treatment by academics and social scientists. The goal of this course is to provide students with a comprehensive understanding of the social economy, the core activity area of the social entrepreneur, by addressing some important topics of social economy. | |||||||||
| GSP5241 | Spatial Modeling for Social Science Research | 3 | 6 | Major | Master/Doctor | Public Administration | - | No | |
| Spatial dependence is prevalent in the society. Geographically proximate individuals, groups, and localities have similar characteristics and behave similarly via spillover effect. Also, the local governments that are closely located often pursue similar policy directions as they are affected by each other. This class aims to explore how to apply spatial dependence in social science research. The class topics include the concept and origin of spatial dependence, global/local spatial analysis, visualization of spatial dependence, and various spatial regression models such as spatial lag, spatial error, geographically weighted regression, and spatial Durbin. | |||||||||
| MCJ5122 | Network Analysis for Communication Research | 3 | 6 | Major | Master/Doctor | 1-4 | Media and Communication | - | No |
| The primary goal of this course is to explore theories and concepts of network analysis in communication perspectives. Moreover, this course will practice with data and network analysis programs to show how network analysis can be used to understand various communication phenomena and problems. | |||||||||
| MCJ5128 | Meta-Analysis | 3 | 6 | Major | Master/Doctor | 2-4 | Media and Communication | - | No |
| Meta-analysis refers to the quantitative analysis of study outcomes. Meta-analysis consists of a collection of techniques that attempt to analyze and integrate effect sizes (indices of the association between an independent variable and a dependent variable) that accrue from research studies. This course deals with the process of performing meta-analysis and how to interpret analysis results. Students will have an opportunity to conduct meta-analysis on research topics of interest using meta-analysis software and to write a research paper based on the analysis results. | |||||||||
| PSD5113 | Future Social Risks and Political Theory | 3 | 6 | Major | Master/Doctor | Political Science | - | No | |
| This course diescusses the new developments in future, the risks they bring about and proper strategies for coping with them. The topics include the climate changes, natural disastors, demographic changes, new discoveries and developments in sciences and technologies and their impacts on our society. The goal of this course is to deliberate on how to react to those changes and challenges properly, which may determine the tufure of individual societies as well humankind. | |||||||||