For more details on the courses, please refer to the Course Catalog
| Code | Course Title | Credit | Learning Time | Division | Degree | Grade | Note | Language | Availability |
|---|---|---|---|---|---|---|---|---|---|
| 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. | |||||||||
| CHS2015 | AI-based Neuroscience and Neurotechnology | 3 | 6 | Major | Bachelor | 1-4 | Challenge Semester | Korean | Yes |
| 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, neuromarketing, neurolinguistics, neuroergonomics, etc.), AI applications to advance neuroscience/engineering, 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 understand the state-of-the-art of AI and its applications to advance neuroscience; ③ articulate the domains and contexts in which cognitive neuroscience and neurotechnology may be effective; ④ develop an ability to lay out the open questions and address challenges in cognitive neuroscience and neurotechnology research today;and ⑤ prepare themselves to be more knowledgeable and proficient professionals. | |||||||||
| CHS2017 | A new human, phono sapiens Experience Design | 3 | 6 | Major | Bachelor | 1-4 | Challenge Semester | Korean | Yes |
| 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. | |||||||||
| CHS7004 | Thesis writing in humanities and social sciences using Python | 3 | 6 | Major | Bachelor/Master/Doctor | Challenge Semester | Korean | Yes | |
| 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. | |||||||||
| CLA3001 | Liberal arts Co-op 1 | 2 | 4 | Major | Bachelor | 3-4 | Liberal Arts | - | No |
| Field practice to utilize knowledge accumulated from classroom studies for real world problems(for 2 weeks) | |||||||||
| CLA3002 | Liberal arts Co-op2 | 3 | 6 | Major | Bachelor | 3-4 | Liberal Arts | - | No |
| Field practice to utilize knowledge accumulated from classroom studies for real world problems(for 4 weeks) | |||||||||
| CLA3003 | Liberal arts Co-op3 | 4 | 8 | Major | Bachelor | 3-4 | Liberal Arts | - | No |
| Field practice to utilize knowledge accumulated from classroom studies for real world problems(for 6 weeks) | |||||||||
| CLA3004 | Liberal arts Co-op4 | 5 | 10 | Major | Bachelor | 3-4 | Liberal Arts | - | No |
| Field practice to utilize knowledge accumulated from classroom studies for real world problems(for 8 weeks) | |||||||||
| CLA3005 | Liberal arts Co-op5 | 9 | 18 | Major | Bachelor | 3-4 | Liberal Arts | Korean | Yes |
| Field practice to utilize knowledge accumulated from classroom studies for real world problems(for 24 weeks) | |||||||||
| CLA3103 | Liberal Arts Individual Research3 | 2 | 4 | Major | Bachelor | 2-4 | Liberal Arts | - | No |
| This is an independent study course for students who have finished an excellent accomplishment of the course requirements and designed for giving credits which make an excellent record to the students for their research works. | |||||||||
| CLA3108 | Introduction to AI for Humanities | 3 | 6 | Major | Bachelor | Liberal Arts | - | No | |
| The goals of introducing the field of Artificial Intelligence to students from the humanities is to disseminate knowledge and create awareness on the biggest buzz word going around today. The fundamentals include notions of rationality, knowledge representa-tion and reasoning, machine learning and ethics. Students will be exposed to the enormity of the field which does not only involve hot topics such as deep learning and smart robots. Further, many well-known success stories of AI will be discussed to complement all the hype that has been surrounding this area, from games to curing dis-eases. Finally and perhaps most importantly, students will debate on on the philosophi-cal and ethical issues pertaining to the development of AI solutions. Using real world examples where lessons have been learnt, students will understand the need for and implications of using AI responsibly. The course contents will require NO programming, major algorithms will be explained broadly without requiring students to perform any calculations. - Introduction - Agents, rationality, strong vs. weak AI - Search and Problem Solving - Uninformed searches, informed searches, local search, logic - Machine Learning - Supervised learning, unsupervised learning, neural networks and deep learning - Applications - Robotics, computer vision, natural language processing - Ethics - Case studies of misuse of AI, dilemmas when designing AI systems | |||||||||
| DSC2004 | Data Science and Python | 3 | 6 | Major | Bachelor | Data Science | English,Korean | Yes | |
| In this course, students acquire basic understanding and skills of python scripting tool that is being widely used for data analysis. Specifically, students learn how to use a variety of python libraries useful for data analysis and visualization. | |||||||||
| DSC2005 | Data Science and R | 3 | 6 | Major | Bachelor | Data Science | English | Yes | |
| This course introduces R, a basic for data analysis. R is a language for statistical computing and graphics including data manipulation and graphical display, and provides a wide variety of statistical (linear and nonlinear modelling, classical statistical tests, clustering, and etc.). This course focuses on understanding statistical concepts and practices of R. | |||||||||
| ENG2019 | Introduction to Applied Linguistics | 3 | 6 | Major | Bachelor | 2-3 | English Language and Literature | English | Yes |
| This course examines topics and issues in applied linguistics. Current theories and trends in various sub-field of applied English linguistics will be covered which includes theories to English acquisition, language processing, discourse, English education and English language testing. Application of basic theoretical knowledge to practical and complex research question will be the focus of each lesson. | |||||||||
| ENG3019 | Theories of Language Acquisition and Education | 3 | 6 | Major | Bachelor | 3-4 | English Language and Literature | English | Yes |
| Mechanisms for the 2nd language learning/acquisition are discussed from the perspectives of linguistic theories, phychological learning theories and the findings from the observations of child native languge acquisition processes. Based on the study of 2nd language acquisition, teaching methods are discussed as its application to English education. | |||||||||