For more details on the courses, please refer to the Course Catalog
| Code | Course Title | Credit | Learning Time | Division | Degree | Grade | Note | Language | Availability |
|---|---|---|---|---|---|---|---|---|---|
| BUS2003 | Marketing Management | 3 | 6 | Major | Bachelor | 1-4 | Business Administration | Korean,English,Korean | Yes |
| Emphasis is placed on the planning, analysis and control of marketing activities with particular focus on product, promotion, pricing and distribution in marketing management. | |||||||||
| BUS2004 | Marketing Strategy | 3 | 6 | Major | Bachelor | 1-4 | Business Administration | Korean | Yes |
| The course is designed to provide a broad understanding of marketing strategy problems, analysis, decision-making, and evaluation. We will focus on - integration of concepts in this and previous marketing course - application to realistic situations - oral and written skills - quantitative analysis | |||||||||
| BUS3008 | Strategic Management | 3 | 6 | Major | Bachelor | 1-4 | Business Administration | Korean,English,Korean | Yes |
| Strategy formulation and implementation are core management functions. This course deals with theory, practice and techniques of strageic management. | |||||||||
| CHS2002 | Data Science and Social Analytics | 1 | 2 | Major | Bachelor | 1-4 | Challenge Semester | - | No |
| This course is intended to examine human behaviors and social phenomena through the lens of data science. Students also may learn online data collection and analysis in social media spaces. It deals with both theory and practice, but relative portion may change in each semester without prior notice. | |||||||||
| CHS2003 | Robust System Design with Big Data Analytics and Artificial Intelligence | 2 | 4 | Major | Bachelor | 1-4 | Challenge Semester | - | No |
| In this course, the fundamental theories and methodologies on big-data analytics and artificial intelligence (AI) algorithms for prognostics and health management (PHM) of engineering systems are mainly covered. More specifically, the reliability analysis, sensor-based big-data collection, signal processing, statistical feature extraction and selection, and AI-based modeling are studied, and the hands-on practices are also carried out. In addition, various case examples are introduced to study the robust engineering system design using the big-data analytics and AI algorithms. | |||||||||
| CHS2009 | Creative Ideation | 2 | 4 | Major | Bachelor | 1-4 | Challenge Semester | - | No |
| Most people think that creativity is closely related to something new, unique and original. But we have no idea how to do if we actually think up creative ideas, which has never been existed, on our own. Let's take note of the well-known old saying, there is nothing new under the sun. We should change our perspective on creativity. There is common and distinct patterns in those things considered to be creative. This course introduce the common patterns of creative ideation with a lot of examples. Major topics include systematic inventive thinking, creative ideation codes, biomimicry, creativity in culture and arts. | |||||||||
| CHS2012 | IoT Project | 2 | 4 | Major | Bachelor | 1-4 | Challenge Semester | - | No |
| It is a course for students who are not familiar with software and hardware, but who are interested in Internet of Things area. It aims to provide easy and convenient steps of the area, including education of C language basics and various digital/analog sensor control conducted with a toolkit such as Arduino. Communication skills and cooperative spirit can be obtained by carrying out IoT projects through group activities. | |||||||||
| CHS2013 | The Convergence of Cognitive Neuroscience and Neurotechnology with Humanities and Social Sciences | 3 | 6 | Major | Bachelor | 1-4 | Challenge Semester | - | No |
| This course will introduce fundamentals of how human brain works and the state-of-the-art of neuroscience research. This course will cover the convergence of cognitive neuroscience and neurotechnology with humanities and social sciences (e.g., brain-computer interface, neuroscience-based cognitive computing, neuroergonomics, etc.), their applications and future directions through class discussions. This course aims for students to 1) understand the literature in the fields of cognitive neuroscience and neurotechnology based on the understanding of humanities and social sciences; 2) articulate the domains and contexts in which cognitive neuroscience and neurotechnology may be effective; 3) develop an ability to lay out the open questions and address challenges in cognitive neuroscience and neurotechnology research today; and 4) prepare them to be more knowledgeable and proficient professionals. | |||||||||
| 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. | |||||||||
| CHS5006 | Optimization and performance evaluation of 3D printing | 3 | 6 | Major | Master/Doctor | 1-4 | Challenge Semester | - | No |
| Evolution of 3D printing application area is slow due to difficulty in developing contents, optimization and evaluation deposit process. We will discuss optimization techniques and evaluation of deposit process for DED based powder metal 3D printing. A real data set will be used for application of theory learned from the class. Furthermore, deep learning and machine learning techniques will be also covered. | |||||||||
| 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. | |||||||||
| 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 | |||||||||
| 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. | |||||||||