For more details on the courses, please refer to the Course Catalog
| Code | Course Title | Credit | Learning Time | Division | Degree | Grade | Note | Language | Availability |
|---|---|---|---|---|---|---|---|---|---|
| CCS2001 | A View of Culture | 3 | 6 | Major | Bachelor | 2-3 | Cross-cultural Studies | Korean | Yes |
| It aims to provide the fundamental cultural concepts for comparative culture. Through various ways of analyzing and understanding culture, students are encouraged to understand culture from various viewpoints and to establish a basic view of culture. | |||||||||
| CCS3011 | Techno-Cultural Studies | 3 | 6 | Major | Bachelor | 3-4 | Cross-cultural Studies | Korean | Yes |
| This course focuses on learning the viewpoint of Techno-Cultural studiest through various theories and modern phenomenon. After exploring how technology has influenced on the change of society from the basis, we expect students will expand the range of thought to individual subject and object. We will go through philosophy of technology and handle 'artificial intelligence', 'post-human', 'techno feminism', from the perspective of 'science and technology studies' and 'cultural studies'. | |||||||||
| CEK5123 | Reading of the Essential Writings of Hundred Schools of Thoughts(zhuzi baijia) | 3 | 6 | Major | Master/Doctor | 1-4 | Confucian Studies, Eastern Philosophy and Korean Philosophy | - | No |
| This course aims to investigate philosophical differences and similarities between pre-Qin thoughts including Confucianism, Mohism(墨家, Mojia), Daoism(道家, Daojia) and Legalism(法家, Fajia) by reading their original texts widely. Students thus would be able to learn the basic philosophical foundations of East Asian Philosophy. Through this, students are to research in priority the central and philosophical themes, which constituted Hundred Schools of Thought, such as the transition of the relationship between Heaven(天, Tien) and Human(人, Ren), differences in ethical point of views, and its corresponding changes in social ideologies, etc. | |||||||||
| 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 | Korean | Yes |
| 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. | |||||||||
| 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. | |||||||||
| CHS2017 | A new human, phono sapiens Experience Design | 3 | 6 | Major | Bachelor | 1-4 | Challenge Semester | - | No |
| As humans started using smartphones, they are experiencing changes in consumption psychology, consumer behavior, and market ecosystems due to rapidly changing lifestyles. This represents a new type of human, the main protagonist of the revolution, called Phono Sapiens. As consumption civilization changes, we learn about digital transformation and changes in business models driven by the development and evolution of big data, artificial intelligence, and digital platforms. We analyze and learn the direction of digital experience design (Digital Experience Design) based on digital transformation. Companies provide and understand the direction of new business innovation and change in accordance with rapidly changing trends for Phono Sapiens, the new consumers. | |||||||||
| CHS2019 | Information and Communication Technology Based on Quantum Mechanics | 1 | 2 | Major | Bachelor | Challenge Semester | Korean | Yes | |
| In the first half, basic physics lectures such as electron and photon, particle and wave duality, quantum superposition, entanglement, uncertainty principle, quantum tunneling effect, and Schrödinger equation, Maxwell's wave equation, and basic mathematics lectures such as Hilbert space, Bloch sphere, and bra and ket vector, which are essential for understanding qubits and quantum superposition, are fundamentally educated for understanding the information and communication technologies based on quantum mechanics. In the second half, current five types of qubit generation methods based on mechanical conservation using inductor and capacitor, quantum gates, quantum circuits, their quantum computer applications, sensitivity increased quantum sensors, and basic principles and current technologies of innovative quantum cryptography and quantum teleportation are taught. | |||||||||
| CHS5005 | AI Startup and Entrepreneurship | 3 | 6 | Major | Master/Doctor | 1-4 | Challenge Semester | - | No |
| Recent years have witnessed a rapid increase in the number of so-called AI startups with AI as their core value, as the scope of AI's application across all industries has expanded significantly. This is gaining popularity not only in Korea, but globally as well. However, there are no theoretical or empirical guidelines regarding the entrepreneurial skills and business models that AI startups in a hypercompetitive market should possess. It is extremely harsh for those AI startups that are actually traditional businesses dressed up to look like they use AI to to succeed in a very competitive market. For AI startups with inadequate business acumen, gaining a foothold on the market is also a daunting task. By focusing on the following three goals, henceforth, this course aims to assist the growing number of AI startups with their challenges. Firstly, it categorizes the various possible business models for AI startup companies. Secondly, it then examines some of the most prominent domestic and international cases to illustrate the various types of entrepreneurship that AI startups require to thrive. Thirdly, a hypothetical AI startup is created, on a team basis, using real-world software such as Landbot, Stable Diffusion, and a number of no-code ML/DL (machhine learning/deep learning). Then its business model and entrepreneurship are established; and its efficacy is evaluated. | |||||||||
| 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 | |||||||||
| CHS7007 | AI-Based Media Text Comprehension | 3 | 6 | Major | Bachelor/Master/Doctor | Challenge Semester | Korean | Yes | |
| This course aims to equip students with the ability to critically analyze and understand various forms of media texts—such as news, advertisements, films, and social media—through the use of artificial intelligence (AI). Students will learn techniques in natural language processing (NLP), including sentiment analysis, keyword extraction, and text summarization, as well as methods for analyzing visual content using AI models like CNNs and GANs. The course also addresses issues of trustworthiness and ethical concerns related to AI-generated content. Combining theoretical instruction with practical application, students will complete hands-on assignments and projects using Python-based AI tools such as GPT and Gemini AI. | |||||||||
| CHS7007 | AI-Based Media Text Comprehension | 3 | 6 | Major | Bachelor/Master/Doctor | Challenge Semester | Korean | Yes | |
| This course aims to equip students with the ability to critically analyze and understand various forms of media texts—such as news, advertisements, films, and social media—through the use of artificial intelligence (AI). Students will learn techniques in natural language processing (NLP), including sentiment analysis, keyword extraction, and text summarization, as well as methods for analyzing visual content using AI models like CNNs and GANs. The course also addresses issues of trustworthiness and ethical concerns related to AI-generated content. Combining theoretical instruction with practical application, students will complete hands-on assignments and projects using Python-based AI tools such as GPT and Gemini AI. | |||||||||