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 | |||||||||
| CHS7005 | Consumer Neuroscience | 3 | 6 | Major | Bachelor/Master/Doctor | Challenge Semester | - | No | |
| A new market and consumer research methodology, consumer neuroscience method, will be explored in this class. Understanding consumers’ brain responses to brand using eyetracker and functional near infraredspectroscopy experiments is a goal of this study. Eyetracking and fNIRS will provide a new means of measuring brand equity as consumers’ brain responses will reflect their attitude, engagement, and and loyalty. | |||||||||
| 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. | |||||||||
| 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. | |||||||||
| CON4001 | Consumer Internship2 | 3 | 6 | Major | Bachelor/Master | 1-4 | - | No | |
| Learn and practice methods of consumer-related big data analysis including text mining, opinion mining, keyword analysis, and logit analysis, and provide insight to solve the problems of consumers. | |||||||||
| CON4002 | Consumer Internship3 | 4 | 8 | Major | Bachelor/Master | 1-4 | - | No | |
| Learn and practice methods of consumer-related big data analysis including text mining, opinion mining, keyword analysis, and logit analysis, and provide insight to solve the problems of consumers. | |||||||||
| CON4003 | Consumer Internship4 | 5 | 10 | Major | Bachelor/Master | 1-4 | Korean | Yes | |
| Learn and practice methods of consumer-related big data analysis including text mining, opinion mining, keyword analysis, and logit analysis, and provide insight to solve the problems of consumers. | |||||||||
| CON4010 | Prosumer and Platform Economy | 3 | 6 | Major | Bachelor/Master | - | No | ||
| Based on understanding the fundamentals of platform economy and the role of consumers as a provider and buyer, evaluate current platform economy, and pursue improvement of platform economy in terms of economic well-being of prosumers. | |||||||||
| CON4012 | Consumer Dispute | 3 | 6 | Major | Bachelor/Master | - | No | ||
| This course focuses on dispute resolution procedures, legal regulations, mediation, and negotiation skills from the perspective of consumer protection and rights enhancement. Its goal is to equip students with the ability to analyze consumer issues and propose practical solutions. Through this course, students will gain both theoretical knowledge and practical skills in consumer dispute resolution, fostering the capabilities needed to contribute to consumer empowerment. Additionally, they will develop communication and problem-solving skills essential for understanding and applying collaborative approaches in the dispute resolution process. | |||||||||
| CON4013 | Artificial Intelligence Data Analytics | 3 | 6 | Major | Bachelor/Master | 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 | 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 | 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. | |||||||||
| CON4016 | AI-Driven Consumer Market Analysis | 3 | 6 | Major | Bachelor/Master | Korean | Yes | ||
| This course aims to develop students' competency in systematically analyzing consumer market trends and data using artificial intelligence (AI) technologies and making data-driven decisions. Students will learn AI-based data collection and analytical techniques to identify consumers' latent needs and to comprehensively analyze market environments and consumer behavior. Based on insights derived from data analysis, students will formulate brand strategies that enhance consumers' quality of life while creating sustainable value. Through this course, students will develop data-driven market analysis competencies and strategic problem-solving skills essential for product planning and marketing practice. | |||||||||
| CON4017 | AI-Integrated Sustainable Product Development | 3 | 6 | Major | Bachelor/Master | Korean | Yes | ||
| This course aims to move beyond designing tangible and intangible products that reflect consumer needs and experiences, focusing on sustainable product development that accounts for environmental and social responsibilities. By leveraging AI as a core tool, students will perform advanced product analysis, data-driven optimization, and rapid prototyping, aiming for the humanization of technology and innovative consumer value creation. Through human-centered design, the course cultivates core competencies to foster active consumer participation as value co-creators, ultimately promoting consumer sovereignty and enhancing the overall quality of life. | |||||||||