For more details on the courses, please refer to the Course Catalog
| Code | Course Title | Credit | Learning Time | Division | Degree | Grade | Note | Language | Availability |
|---|---|---|---|---|---|---|---|---|---|
| BPC5013 | 3D printed chip design and fabrication | 3 | 6 | Major | Master/Doctor | 1-8 | Biophysics | - | No |
| The main objective of the course is to introduce 3D bioprinting used in lab-on-chips to graduate students having a background in engineering. The course should familiarize the students with the techniques used in 3D bioprinting and show them how 3D printing technology pervades throughout various regenerative medicine. | |||||||||
| BPC5014 | IQB Colloquim1 | 3 | 6 | Major | Master/Doctor | 1-8 | Biophysics | English | Yes |
| The main objective of the course is for IQB students to learn research topics in multiple areas, widen their insights, and consequently elevate their research. This course is composed of weekly seminars provided by speakers from SKKU and others with introductory to and recent publications in their multidisciplinary areas. | |||||||||
| BPC5015 | IQB Colloquim2 | 3 | 6 | Major | Master/Doctor | 1-8 | Biophysics | English | Yes |
| The main objective of the course is for IQB students to learn research topics in multiple areas, widen their insights, and consequently elevate their research. This course is composed of weekly seminars provided by speakers from SKKU and others with introductory to and recent publications in their multidisciplinary areas. | |||||||||
| 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 | |||||||||
| CHS7006 | A new human AI Sapiens Experience Design | 3 | 6 | Major | Bachelor/Master/Doctor | Challenge Semester | Korean | Yes | |
| This course analyzes the impact of artificial intelligence (AI), big data, and digital platforms on consumer behavior and market ecosystems in the rapidly changing digital environment. Building upon this analysis, it explores experience design principles suited for the new human era, ‘AI Sapiens’. Focusing on human-AI interaction, it investigates user experience (UX) and service design strategies to help businesses and society adapt. It examines how AI influences consumer psychology and behavior, studies AI-driven market shifts, and analyzes digital transformation cases. The course also covers AI/data-driven UX/UI design concepts and applications of chatbots, voice recognition, and recommendation systems. It explores related technologies such as 5G, IoT, autonomous vehicles, and smart factories, while addressing ethics, privacy, and human-centered design. Through hands-on exercises and project-based learning, students design AI-based services, analyze real-world cases, and propose experience design solutions. This cultivates creative problem-solving skills and prepares students to become AI experience design experts who meet business and societal needs in the digital transformation era. | |||||||||
| CHS7006 | A new human AI Sapiens Experience Design | 3 | 6 | Major | Bachelor/Master/Doctor | Challenge Semester | Korean | Yes | |
| This course analyzes the impact of artificial intelligence (AI), big data, and digital platforms on consumer behavior and market ecosystems in the rapidly changing digital environment. Building upon this analysis, it explores experience design principles suited for the new human era, ‘AI Sapiens’. Focusing on human-AI interaction, it investigates user experience (UX) and service design strategies to help businesses and society adapt. It examines how AI influences consumer psychology and behavior, studies AI-driven market shifts, and analyzes digital transformation cases. The course also covers AI/data-driven UX/UI design concepts and applications of chatbots, voice recognition, and recommendation systems. It explores related technologies such as 5G, IoT, autonomous vehicles, and smart factories, while addressing ethics, privacy, and human-centered design. Through hands-on exercises and project-based learning, students design AI-based services, analyze real-world cases, and propose experience design solutions. This cultivates creative problem-solving skills and prepares students to become AI experience design experts who meet business and societal needs in the digital transformation era. | |||||||||
| 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. | |||||||||
| COV7006 | Academic Writing in the AI Era | 1 | 2 | Major | Master/Doctor | SKKU Institute for Convergence | Korean | Yes | |
| This 1-credit online graduate course guides dissertation-track students to responsibly utilize generative AI. Developed under the BIGs 2.0 initiative ("Graduate Common Curriculum Development"), it inherits the prerequisite structure for dissertation defense while integrating AI practical applications with research ethics. Based on the Generative AI Research Ethics Guidelines for University Researchers (MOE, NRF, KCURE, June 2026), the 16-week curriculum covers the entire research lifecycle across four monthly modules: -Month 1: AI fundamentals, limitations, and core research ethics (FFP & authorship). -Month 2: AI application principles in literature search, topic formulation, and data analysis. -Month 3: Academic writing combining drafting, refinement, and cross-validation. -Month 4: AI usage disclosure, copyright issues, and final verification responsibility. Research ethics forms a key pillar, occupying roughly one-third of the term. By focusing on core principles, the course builds adaptive resilience to evolving tools and proactively prevents misconducts like fake citations or unauthorized manuscript uploads. Ultimately, as part of the core graduate curriculum, it aims to foster researchers who use AI not just proficiently, but responsibly. | |||||||||
| COV7007 | Academic Paper Writing in the Age of AI | 1 | 2 | Major | Master/Doctor | SKKU Institute for Convergence | - | No | |
| This course was developed for graduate students preparing manuscripts for scientific journals within an academic writing environment increasingly shaped by generative AI. Its foundational premise is that while AI can assist with linguistic expression (English drafting), it cannot replace logical reasoning, critical decision-making, and academic accountability. As language barriers diminish, a researcher's primary competitive advantage has shifted from superficial expression to rigorous logic and judgment. This course aims to systematically train students in the core competencies required during this paradigm shift. | |||||||||
| EAM7001 | Plasma Processes and Equipment | 3 | 6 | Major | Bachelor/Master/Doctor | 3-4 | Advanced Materials Science and Engineering | Korean | Yes |
| This class will discuss theoretical and experimental backgrounds on processing, diagnostic, and equipment technologies related to plasma deposition and etching applied to semiconductor, displays, and various nanodevice processing. The contents are as follows; 1) Gas Collision Processes, 2) Vacuum and Parts, 3) Plasma Technology, 4) DC, RF, High Density Plasmas, 5) Plasma Dignostics, 6) Plasma Deposition, 7) Plasma Etching, 8) Seminar on Recent Plasma Application Technologies | |||||||||
| EAM7001 | Plasma Processes and Equipment | 3 | 6 | Major | Bachelor/Master/Doctor | 3-4 | Advanced Materials Science and Engineering | Korean | Yes |
| This class will discuss theoretical and experimental backgrounds on processing, diagnostic, and equipment technologies related to plasma deposition and etching applied to semiconductor, displays, and various nanodevice processing. The contents are as follows; 1) Gas Collision Processes, 2) Vacuum and Parts, 3) Plasma Technology, 4) DC, RF, High Density Plasmas, 5) Plasma Dignostics, 6) Plasma Deposition, 7) Plasma Etching, 8) Seminar on Recent Plasma Application Technologies | |||||||||
| ECE4247 | Power Electronics System Analysis | 3 | 6 | Major | Bachelor/Master | 1-4 | Electrical and Computer Engineering | - | No |
| Inverters and converters play an important role to operate fuel cell systems, hybrid electric vehicles, and etc. In this subject, design and control of various power conversion circuits according to the application conditions. Completing this subject, one can handle hardwares and softwares for power conversion circuits and can utilize the theory and technique for industry applications | |||||||||