For more details on the courses, please refer to the Course Catalog
| Code | Course Title | Credit | Learning Time | Division | Degree | Grade | Note | Language | Availability |
|---|---|---|---|---|---|---|---|---|---|
| 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 | |||||||||
| ERC2010 | Fundamental Mathematics in Engineering1 | 3 | 6 | Major | Bachelor | Engineering | Korean,English,Korean | Yes | |
| This course focuses on ordinary differential equations (ODEs) and complex analysis with engineering applications in mind. Topics include: first-order ODEs, second-order ODEs, Laplace transform, systems of ODEs, Cauchy-Riemann equation, and conformal mapping. | |||||||||
| ERC2011 | Fundamental Mathematics in Engineering2 | 3 | 6 | Major | Bachelor | Engineering | Korean,English,Korean | Yes | |
| This course starts with core concepts in linear algebra, especially those useful in various engineering disciplines. Next, several applications of partial differential equations (PDEs) will be introduced with their solution techniques. While doing so, we will investigate the theoretical and practical importance of Fourier series. | |||||||||
| 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. | |||||||||
| ERC3001 | Global Capstone Design | 3 | 6 | Major | Bachelor | 3-4 | Engineering | Korean | Yes |
| This course covers the innovative design process based on international and interdisciplinary design teams by exploring users' various needs, defining design problems, reflecting practical constraints, generating diverse concepts, evaluating and refining final solutions and prototyping. The multidisciplinary design theories and methodologies will be addressed to guide the design process. Therefore, in this course, core capabilities for next generation engineers such as global competitiveness, innovation ability and interdisciplinary capstone design ability can be significantly fostered. | |||||||||
| ERC3002 | Engineering Research Project Ⅰ | 2 | 4 | Major | Bachelor | 2-4 | Engineering | Korean | Yes |
| This course provides the undergraduate students the research experience for the state-of-the-art technology in modern engineering. | |||||||||
| ERC3003 | Engineering Research Project Ⅱ | 2 | 4 | Major | Bachelor | 2-4 | Engineering | Korean | Yes |
| This course provides the undergraduate students the research experience for the state-of-the-art technology in modern engineering. | |||||||||
| ERC3004 | Engineering Research Project Ⅲ | 2 | 4 | Major | Bachelor | 2-4 | Engineering | Korean | Yes |
| This course provides the undergraduate students the research experience for the state-of-the-art technology in modern engineering. | |||||||||
| ERC3005 | Engineering Research Project Ⅳ | 2 | 4 | Major | Bachelor | 2-4 | Engineering | Korean | Yes |
| This course provides the undergraduate students the research experience for the state-of-the-art technology in modern engineering. | |||||||||
| ERC3010 | Intellectual Property Strategy for Startup | 3 | 6 | Major | Bachelor | 2-4 | Engineering | - | No |
| - In the era of the Fourth Industrial Revolution, intellectual property(IP), which is a high-level creative work, is getting more and more important, and creating IP through understanding of IP and applying it to business is one area of core competence. It requires learning about creation and management of IP and application to start-up business through in-deepth understanding of IP. - This course deals with the understanding of IP, how to create and manage IP, and the whole theoretical and practical know-how on the application of business based on managed IP(start-up business). - Detail learning contents include the concept and type of IP (industrial property rights, copyright, new intellectual property rights), creation of IP, management of IP, valuation of IP, trading of IP property, business creation, and IP-based start-up. - To increase the success factor of IP-based start-up by empirically analyzing the cases of successful and failed IP-based start-ups, and to acquire the practical know-how of IP-based property start-ups directly in representative of IP-based start-ups. | |||||||||
| ERC3011 | Industry Collaborative Capstone Design | 3 | 6 | Major | Bachelor | 3-4 | Engineering | - | No |
| This course is a senior level capstone design course in the College of Engineering, School of Information & Communication Engineering and the College of Software, the College of Natural Science. This capstone design course emphasizes the industry-based problem solving task through interdisciplinary team project. The students of this course will discover industry and customer’s needs, and explore industry-based problem issues, and study on interdisciplinary knowledge and theory. Also, they will try to apply the diverse engineering sciences and humanistic knowledge to the problem and reflect the actual industry situation and various consideration, explore various open-ended solutions, improve their best solution, and communicate their results each other. Each team will consist of students from several different departments, and be supported by professors and industry-expert(mentor). | |||||||||
| ERC3012 | Patent Idea Search and Application | 3 | 6 | Major | Bachelor | 3-4 | Engineering | - | No |
| 1.Objectives The purpose of this course is to cultivate technology exploration skills of future engineering students through theory study and practice of prior art research in science and technology field. Through this course, you can develop basic knowledge and expertise about intellectual property rights. 2. Class contents (1) Understanding of intellectual property rights (patent, trademark, design, etc.) - Intellectual Property Overview - Patent registration requirements and application procedure - Exercising intellectual property rights - Interpretation of IP claims (2) Prior art research - Prior art subject (technical literature such as patent, thesis) - Patent utilization method and search strategy in each country - Use of prior art research (review of registration possibility, review of invalid reasons, patent map, etc.) - Use of prior art research database (WIPS, KIPIRIS, etc.) - Keyword search method for prior art research - Prior Art Research Practice (3) Distinguishing the difference between my ideas and technologies - Identify possession technologies and ideas - Research and analysis of technology, industry, and policy trends - Knowing how to use analytics tools (Google, tech trends sites, new business trends sites, government information sites) - Deriving ideas and technology differentiation from prior art - Idea / technology applied product line / commercialization model / direction of research | |||||||||
| ERC3013 | Technology Commercialization Capstone Design | 3 | 6 | Major | Bachelor | Engineering | - | No | |
| This lecture consists of background theory and practice for patent protection and commercialization(technology transfer..) of technologies or engineering ideas. | |||||||||
| ERC3014 | AI in Engineering | 3 | 6 | Major | Bachelor | Engineering | Korean | Yes | |
| This course covers various aspects of Artificial Intelligence (AI) for students of Engineering College. AI has different definitions and applied techniques depending on the fields of application and majors. In this course, the students will learn about the various definitions, basic concepts and methodologies of AI related to Engineering. Generally, AI agreed to the division of AI into know ledge-based(where in telligence are entered or built into the system by gathering the information from experts) and the computational intelligent system(where intelligence are computed into a model based on a lot of past data available). In this course, students learn knowledge-based systems such as rule-based systems (RBS), search algorithms, RBS uncertainty, fuzzy systems, and methodologies of computational intelligence systems such as artificial neural networks, deep neural networks, and genetic algorithms. The course is intended to be theoretical as well as practical. To gain some practical knowledge, students are required to produce prototype systems for knowledge based and computational based AI. In completing the course assignment and tasks, students are required to work individually and/or as a group to be involved in problem-solving and making decisions. Students' knowledge and ability are also tested in case studies, projects, interactive discussions, mid-term examination, and final examination. | |||||||||