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 | 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. | |||||||||
| CON4003 | Consumer Internship4 | 5 | 10 | 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. | |||||||||
| 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 | Korean | Yes | ||
| 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. | |||||||||
| CON5001 | Studies in Consumer Policy | 3 | 6 | Major | Master/Doctor | 1-4 | Korean | Yes | |
| 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 | Korean | Yes | |
| 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. | |||||||||
| CON5010 | Advanced Consumer Economics | 3 | 6 | Major | Master/Doctor | 1-4 | - | No | |
| Learning the neoclassical micro-economic theories and behavioral economic theories to understand the consumer economic choices and identifying the merits and weakness of the theories. Ultimately understanding the current state of household economics by investigating the household income, expenditure, and saving. | |||||||||
| CON5011 | Advanced Analysis of Consumer Needs | 3 | 6 | Major | Master/Doctor | 1-4 | - | No | |
| Understand the levels of consumer needs and the appropriate methods for each level of consumer needs and extend the knowledge and skills for eliciting consumer needs with newly developed human-centered design research methods in order to enhance the abilities of consumer-oriented NPD (New Product Development). | |||||||||
| CON5013 | Advanced Analysis of Consumer Types | 3 | 6 | Major | Master/Doctor | 1-4 | - | No | |
| Understand various ways of classifying consumer types with various perspectives and examine demographic, psychological, behavioral, economical, and cultural characteristics of each type of consumers classified with each perspective. | |||||||||