For more details on the courses, please refer to the Course Catalog
| Code | Course Title | Credit | Learning Time | Division | Degree | Grade | Note | Language | Availability |
|---|---|---|---|---|---|---|---|---|---|
| CHS7001 | Introduction to Blockchain | 3 | 6 | Major | Bachelor/Master/Doctor | Challenge Semester | - | No | |
| This course deals with the basic concept for the overall understanding of the technology called 'blockchain'. We will discuss the purpose of technology and background where blockchain techology has emerged. This course aims to give you the opportunity to think about the limitations and applicability of the technology yourself. You will understand the pros and cons of the two major cryptocurrencies: Bitcoin and Ethereum. In addition, we will discuss the concepts and limitations about consensus algorithm (POW, POS), the scalability of the blockchain, and cryptoeconomics. You will advance your understanding of blockchain technogy through discussions among students about the direction and applicability of the technology. | |||||||||
| 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 | |||||||||
| 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. | |||||||||
| COV3028 | Practical Patent Law for Inventors | 3 | 6 | Major | Bachelor | SKKU Institute for Convergence | - | No | |
| Under conventional education system, a class which teaches capability to do creative invention is divided from a class which teaches capability to help inventors to protect inventions as patents. This class, as commingling such two classes, trains inventors who can exploit their inventions, based on: on the whole process of invention, always the perspective of patent protection must be applied; patent-based perspective can enable inventors make better inventions; a good invention without being a strong patent cannot be commercially successful, etc. Specifically, being taught are: prior art search for establishing direction of R&D; evaluation of patentability of an invention; his invention’s infringement of another’s patent right, agreement of a license contract, etc. In addition, this class enables such students who are preparing the “Patent Attorney” examination to grasp basic concepts of the patent law. | |||||||||
| 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. | |||||||||
| EAM4014 | Global Techno Management | 2 | 4 | Major | Bachelor/Master | 1-4 | Advanced Materials Science and Engineering | - | No |
| The requirement and problem of the real technology in the industrial field are analyzed by inviting specialists and CEOs to learn the ability of solving the industrial problem. Also, management and commencement of an enterprise are studied. | |||||||||
| ECA5301 | Building structure system | 3 | 6 | Major | Master/Doctor | 1-4 | Civil, Architectural and Environmental System Engineering | - | No |
| The characteristics and design methodology of various structure systems that form building structures are studied. How these structure systems are applied to real structures are also studied. | |||||||||
| ERC2006 | Creative Engineering Design | 3 | 6 | Major | Bachelor | 2-3 | Engineering | - | No |
| This course will introduce basic qualities for design engineers that will form the foundation for life-long learning for all engineers through team-based project-based learning. The qualities for designers covered include: creative problem solving ability, visual reasoning and sketching ability, teamwork ability, ability to understand consumer trends and consider user perspectives, ability to represent functions reflecting user requirements and generating concepts to realize such functions, ability to discuss about design contents as well as reporting and presenting. | |||||||||
| ERC2007 | Engineering Numerical Analysis | 3 | 6 | Major | Bachelor | 2-3 | Engineering | Korean,English,Korean | Yes |
| We deal with the major sources of errors in numerical methods, the approximated solution of non-linear equations, the solution of simultaneous linear equations, polynomial interpolation, numerical differentiation, numerical integration, and curve fitting to measured data as a technique to solve the mathematical problems in scientific and engineering area. | |||||||||
| ERC2008 | Introduction to Management of Technology | 3 | 6 | Major | Bachelor | 2-3 | Engineering | Korean,Korean | Yes |
| This subject handles various theory and practical application about MOT. Main contents are Test of technical innovation process, R&D strategy, patent administration, R&D project plan establishment, Market analysis, Technology Assessment, Technology Contract, Technology Management. | |||||||||
| ERC2009 | Interdisciplinary Capstone Design | 3 | 6 | Major | Bachelor | 2-4 | Engineering | - | No |
| This course is a senior level capstone design course in College of Engineering and School of Information & Communication Engineering, emphasizes a real world level of interdisciplinary efforts associated with the industries. The students of this course will explore traditional and new issues, and study on interdisciplinary knowledge and theory. Also, they will try to apply the diverse engineering sciences to the problem, explore and analysis feasible solutions, develop a best solution, and communicate their results each other. Each team will consist of students from several different departments, and be supported by professors from College of | |||||||||
| ERC2012 | Digital Mathematics for Artificial Intelligence | 3 | 6 | Major | Bachelor | Engineering | Korean | Yes | |
| In this course, students will be introduced and learn main fields of Artificial Intelligence (AI) through Mathematics such as Linear Algebra and Probability and Statistics. AI has the basic components as follows; data, models, parameter estimation. Based on data, students will learn main theories of AI; linear regression, dimensionality reduction, density estimation, classification, and so on. Student will learn linear regression and classification of data in AI for finding good model that describe data in Statistics and Linear Algebra. Students will learn dimensionality reduction using principal component analysis throughout linear algebra. In this class, students will learn various areas of AI, based on Linear Algebra and Statistics, such as regression line, least-squares problems, gradient descent, principal component analysis and more. Throughout this course, students will be able to obtain a solid foundation of AI learning. In order to gain knowledge and develop the ability and skills, students are required to work individually and/or in a group for problem solving, case studies, interactive discussions, and midterm and final examinations. Each student will be evaluated based on those learning performances. | |||||||||