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. | |||||||||
| 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. | |||||||||
| CON4004 | Consumer&MarketAnalysis | 3 | 6 | Major | Bachelor/Master | Korean | Yes | ||
| This course provides a well-grounded understanding of consumer market and business strategies that contribute to consumer wellbeing as well as profitability of companies. Specifically, students implement the macro environmental analysis and major companies’ 4P(product, Price, Promotion, Place) analysis, and conduct consumer survey. Based on the results, students practice product development and establishment of marketing strategy. | |||||||||
| CON4005 | Product Anatomy and Consumer Studies | 3 | 6 | Major | Bachelor/Master | Korean | Yes | ||
| To develop capabilities as a product development and planning expert, students learn how to systematically decompose and analyze the components and attributes of products from various angles, and generate ideas for developing consumer-oriented new products. | |||||||||
| 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 | Korean | Yes | ||
| 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 | - | No | ||
| 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. | |||||||||
| CON5001 | Studies in Consumer Policy | 3 | 6 | Major | Master/Doctor | 1-4 | - | No | |
| valuation of consumer policies in Korea by understanding the principles and logics in intermediary roles of government and consumer policies in OECD countries. | |||||||||
| CON5004 | Quantitative Method for Consumer Research | 3 | 6 | Major | Master/Doctor | 1-4 | Korean | Yes | |
| study on research methods and statistical analysis for dissertations and researches in comsumer science. | |||||||||
| CON5006 | Advanced Consumer Decision Making | 3 | 6 | Major | Master/Doctor | 1-4 | - | No | |
| Integrate knowledge from economics, psychology, sociology, and marketing into the study of individual, social, and environmental factors of consumer behavior. Critically analyze evolving individual and societal issues in consumer behavior. | |||||||||