For more details on the courses, please refer to the Course Catalog
| Code | Course Title | Credit | Learning Time | Division | Degree | Grade | Note | Language | Availability |
|---|---|---|---|---|---|---|---|---|---|
| CHS5003 | Social Simulation based on Agent-based Modeling | 3 | 6 | Major | Master/Doctor | Challenge Semester | - | No | |
| The real world system consists of environment and various agents which are some kinds of objects. Each agent decides and acts according to its own decision process, and the system shows complex behaviors through interactions between the components (environment and agents). The social simulation using agent-based modeling is used to mimic the social phenomena (behaviors from interactions between agents), and used in various fields such as transportation, public health, and national defense industry. This course aims to learn the concepts and examples of social simulation using agent-based modeling. focusing on basic probability and statistics, population synthesis, agent-based modeling methodology, and the epidemic simulation. | |||||||||
| 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 | - | No | |
| 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. | |||||||||
| 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. | |||||||||
| 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. | |||||||||
| DBA5034 | Management Information Systems | 3 | 6 | Major | Master/Doctor | 1-4 | Business Administration | English | Yes |
| The competitive strength of contemporary business enterprises hinges on thire capability to manage and utilize business information in an effective manner. To obtain this capability, the management is required to understand IT in the context of management and organization.Students in this course will review the past literature exploring the relationship among IT,management, and organization, and there by understanding the directions of research streams in the field of MIS. | |||||||||
| DBA5036 | Internet Business | 3 | 6 | Major | Master/Doctor | 1-4 | Business Administration | English | Yes |
| The main purpose of this course is to offer students with an opportunity to review the theoretical development in this relatively new area of research. To achieve this goal, the course deals with such issues as basic Internet related technologies, business innovations in functional areas as well as across the firm boundaries, electronic commerce, and others including payment, security, and privacy. | |||||||||
| DBA5080 | Statistics for Business | 3 | 6 | Major | Master/Doctor | 1-4 | Business Administration | Korean | Yes |
| Formulas are not the everyday language of most students. Statistics should be presented in a way that managers learn best. This class presumes no background in Calculus and minimizes the use of mathematical languages. Instead we use extended examples through the class. This class covers probability distribution, sampling distribution, confidence interval, hypothesis testing. We also cover the analysis of variance and regression, using EXCEL, SPSS or SAS. | |||||||||
| DBA5091 | CrossManagementandCurrentIssuesinMarketing | 3 | 6 | Major | Master/Doctor | Business Administration | - | No | |
| This course is designed to explore new research topics in the academic field of marketing by exposing students to new research. Specifically, as the business environment is radically changing, with infusion of new technology, it is required to study how to reinterpret the exiting theories and literature, and how to create new contextual knowledge. This will enable students to develop new research topics and make their research stream more fruitful. | |||||||||
| DES5035 | Design Marketing Strategy | 3 | 6 | Major | Master/Doctor |
1-4
1-4 |
Design | Korean | Yes |
| This course introduce the students to key brand ideas and phenomena and develop their strategy in marketing analysis and planning. | |||||||||
| DES5037 | Design ＆ Management | 3 | 6 | Major | Master/Doctor |
1-4
1-4 |
Design | - | No |
| This course focuse on the strategic thinking for designing in the company. Students developing and implementing company strategies to design output. they will become more skilled as a design manager. | |||||||||
| DES5038 | Design Issue | 3 | 6 | Major | Master/Doctor |
1-4
1-4 |
Design | - | No |
| Study the frameworks and practices of Design Issue. Examine the number of issues for the designers :design system, strategy, Image narrative, etc. Explore the guidelines for selection of issues and develop new issu for the public. | |||||||||
| 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. | |||||||||
| DES5067 | Design Identity | 3 | 6 | Major | Master/Doctor | 1-4 | Design | - | No |
| The objective of this course is research individual brand according to design identity strategy model, and establishes and applies inventive design identity strategy to selected research subject brand. | |||||||||