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 | |||||||||
| 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. | |||||||||
| DES5016 | Environmental Design Seminar | 3 | 6 | Major | Master/Doctor | 1-4 | Design | Korean | Yes |
| This course is study of the relationship between the principle of spartial structure and the supportive image expressed. Particular attention is given to aesthetic aspects including appropriateness of spaces details. | |||||||||
| DES5019 | Special Theory of Environmental Design | 3 | 6 | Major | Master/Doctor | 1-4 | Design | - | No |
| This is an applied course in which concept of indoor/outdoor spartial design are developed to advanced application of design technology in structure, materials, form through the examination of space types. | |||||||||
| DES5064 | Case Study of Design Management Strategy | 3 | 6 | Major | Master/Doctor | 1-4 | Design | Korean | Yes |
| Case Study of Product and Creative Strategy thru Design Management by Samsung Design Management Center. | |||||||||
| DES5065 | Design Intellectual Property Rights | 3 | 6 | Major | Master/Doctor | 1-4 | Design | Korean | Yes |
| Offer on and off line Design Intellectual Property contents by an agreement with a International Intellectual Property Rights researcher from the Korean Intellectual Property Office. | |||||||||
| DES5083 | Creative Design Thinking | 3 | 6 | Major | Master/Doctor | 1-4 | Design | Korean | Yes |
| This class will be analysis Creative itself and practice of trans-media method which are able to explore the innovative solution. After this class students will know how to approach and prepare with Design Thinking for the Consumer goods. | |||||||||
| FDM5072 | Seminar in Fashion Marketing Strategy | 3 | 6 | Major | Master/Doctor | 1-4 | Fashion Design | - | No |
| This course integrates fashion marketing concepts and application to current situations in the fashion business in order to predict fashion marketing strategy. | |||||||||
| FDM5075 | Seminar in Fashion Distribution strategy | 3 | 6 | Major | Master/Doctor | 1-4 | Fashion Design | - | No |
| This course is designed to introduce students with a strategy of Fashion Distribution. Major topics of this course are distribution environment, distribution channel, trading area analysis, channel conflict and logistics in fashion business | |||||||||
| IID5001 | Understanding Design & Big Data | 3 | 6 | Major | Master/Doctor | 1-4 | Korean | Yes | |
| Learn the concept and theoretical framework of big data and focus on learning use cases. To do this, acquire the statistical theory needed to handle big data and (R) develop the ability to leverage design-related social data through real-world data analysis using big data-related statistical packages. | |||||||||
| IID5002 | Seminar in Design Communication and Network Theory | 3 | 6 | Major | Master/Doctor | 1-4 | - | No | |
| The aim is to explore the understanding of the characteristics of hyperconnected societies that have been accelerating since the digital age, human-centered design based on network theory and interrelationships in communication. To this end, I learned network theory and analysis method through connection to acquire insight and analysis ability about design communication within connected network. | |||||||||
| IID5003 | Big Data Analysis and Visualization | 3 | 6 | Major | Master/Doctor | 1-4 | - | No | |
| Learn theories and methods to logically understand consumer needs through big data analysis using Amazon or Microsoft's data commercial tools. | |||||||||
| IID5004 | Data Based Design Practice | 3 | 6 | Major | Master/Doctor | 1-4 | Korean | Yes | |
| Practicing using unstructured data obtained by analyzing big data, such as consumer needs and behavioral patterns, rather than data through real-time surveys. | |||||||||
| IID5005 | Data Science for Designer | 3 | 6 | Major | Master/Doctor | 1-4 | - | No | |
| The goal is to develop a basic understanding of the basic concepts of data science that designers must know and a basic ability to analyze practical data. To learn the process of gathering, analyzing, and processing data on their behavior, experience, etc. by focusing on real users at the center of their design strategy. | |||||||||