For more details on the courses, please refer to the Course Catalog
| Code | Course Title | Credit | Learning Time | Division | Degree | Grade | Note | Language | Availability |
|---|---|---|---|---|---|---|---|---|---|
| AAID001 | Understanding AI and basics in data science | 3 | 6 | Major | Master | 1-5 | Korean | Yes | |
| In this lecture, you will learn the core technologies of artificial intelligence, and identify cases where related technologies are currently used in real industries. In addition, the basic concepts of artificial intelligence technology will be explored. Further, various methods of exploration and optimization, knowledge representation and inference techniques, machine learning methods including deep learning, and planning methods will be outlined. | |||||||||
| AAID002 | Programming basics for applied AI education | 3 | 6 | Major | Master | 1-5 | Korean | Yes | |
| In this lecture, you will learn the basics of programming for convergence education in the AI era. Students taking this course aim to develop the ability to perform basic coding tasks using the Python programming language. Also, Student will create AI convergence education program using learned programming skills. | |||||||||
| AAID003 | Data structure and algorithm for data science | 3 | 6 | Major | Master | 1-5 | Korean | Yes | |
| This is a basic course for problem solving in the field of data science and deals with the analysis of given problems, design of data structures, algorithm development, and implementation techniques. | |||||||||
| AAID004 | Computing thinking and problem solving | 3 | 6 | Major | Master | 1-5 | Korean | Yes | |
| In this course, students learn how to find problems in a given environment using special thought patterns and procedures, solve them, or devise programs for necessary computing. Specifically, this course deals with the concepts of problem decomposition, pattern matching, abstraction, and automation, and aims to apply what students have learned to real situations. | |||||||||
| AAID005 | Special Issues on Machine Learning and Deep Learning | 3 | 6 | Major | Master | 1-5 | Korean | Yes | |
| This course starts with the history of machine learning and deep learning, and learns the concepts and technical terms of each model. In addition, by learning about the latest artificial intelligence neural network models such as CNN, RNN, GAN, and LSTN based on artificial intelligence currently applied, the students learn the theoretical foundation. | |||||||||
| AAID006 | AI Ethics | 3 | 6 | Major | Master | 1-5 | - | No | |
| Today, the development of artificial intelligence provides a lot of convenience to our lives, but there is a controversy over whether artificial intelligence used in various fields will damage human dignity or ethics. The AI Ethics course deals with the development of artificial intelligence as well as appropriate behaviors, mindsets, and social issues that humans must have. | |||||||||
| AAID007 | Virtual Reality Video Processing | 3 | 6 | Major | Master | 1-5 | - | No | |
| This lecture provides the technologies for most recent virtual reality(VR) services. It covers the understanding (1) the international video standards such as MPEG-immersive, (2) the 360-degree video and metadata processing, and (3) the multimedia computing systems for the VR. It also provides the exercises and experiments using standard reference SW, test model for immersive video(TMIV), for understanding overall VR processing systems. | |||||||||
| AAID008 | Analysis of Learning Materials and Methods of AI Convergence Education | 3 | 6 | Major | Master | 1-5 | Korean | Yes | |
| “Analysis of Learning Materials and Teaching Methods of AI Convergence Education” is a course for developing AI convergence education course materials at an appropriate level and acquiring teaching methods suitable for learners. In this course, students learn the process of researching, developing, and evaluating various textbooks for classes that integrate artificial intelligence (AI) not only to core subjects, such as Mathematics, Science, and Information Technology, but also to Humanities and Social Sciences as well as the Arts and Sports. Students also learn about the knowledge and procedures necessary to develop the textbooks necessary for AI convergence education. In particular, the core educational content is to set educational goals and to reorganize and converge subject contents suitable for them. This is a course that covers theories, such as Educational Purpose Theory, Textbook Research Theory, Educational Methodology, and Educational Evaluation Theory. This is a required course for pre- and in-service teachers. This course is intended for the students to analyze teaching materials and textbooks for effective and efficient AI convergence education, and to develop the ability to evaluate textbooks appropriate for AI convergence classes. Therefore, course contents include The Understanding of AI Convergence Education, The Goal of AI Convergence Classes, Learning Contents of AI Convergence Education, Teaching Materials of AI Convergence Education, Teaching-Learning | |||||||||
| AAID009 | AI Convergence Subject-based Practice | 3 | 6 | Major | Master | 1-5 | - | No | |
| This course aims to derive best cases and models of artificial intelligence convergence subject education by exploring examples of various types of AI convergence subject classes currently in progress in the school education field and evaluating them based on specific criteria. In this process, learners need to find out best examples or practice of AI convergence classes by other teachers or discover cases of AI convergence curriculum classes in other countries, find benchmarking points, and apply them to the lessons of individual teachers. | |||||||||
| AAID010 | Instructional design of AI-based | 3 | 6 | Major | Master | 1-5 | Korean | Yes | |
| This course aims to explore how advanced intelligent information technology, including artificial intelligence, can be applied to subject education classes. Let's try to understand and practice the entire process of designing, and developing AI-based classes so that students can conduct different classes by applying artificial intelligence in a specific unit class among the subjects in charge of individual teachers as learners. | |||||||||
| AAID011 | Understanding Big Data Analytics | 3 | 6 | Major | Master | 1-5 | Korean | Yes | |
| This course discusses basics of big data analytics. The topics covering in thiscourse are fundamentals of big data, examining big data types. relation of cloudwith big data, operational big data management system, MapReduce fundamentals,Hadoop foundation and ecosystem, big data warehouses, big data analytics.understanding text analytics, integrating data sources, operationalizing big data andsecurity in big data management. | |||||||||
| AAID012 | Understanding of natural Language Processing | 3 | 6 | Major | Master | 1-5 | Korean,Korean | Yes | |
| Natural Language Processing is one of the important technologies in artificial intelligence convergence. Students can learn natural language processing model based on theory in this course. This course aims to understand the principle by understanding the basic knowledge of the NLP area and further implementing it directly in an online Integrated Development Environment. And this course introduces recent studies related to NLP. | |||||||||
| AAID013 | AI Education Using Physical Computing | 3 | 6 | Major | Master | 1-5 | - | No | |
| In this lecture, we learn about development tools and environments related to open source based microcontroller boards (Arduino). we learn about advanced physical computing techniques closely related with computer engineering and how to apply them to real life. In details, we intensively learn how to connect components through the hardware development of the Arduino project, how to read schematics, how to acquire data sheets, and how to select and use sensors to implement specific functions. | |||||||||
| AAID014 | Visualization of Bigdata | 3 | 6 | Major | Master | 1-5 | Korean | Yes | |
| In the era of big data, a lot of data is generated, and many studies are focused on how to derive meaningful information by analyzing the data collected on the basis of big data. Visualization has become an essential factor not only to summarize data, but also to discover hidden meanings in data and to gain insight into new information indicated by the results of data analysis. Big data visualization refers to the visual expression of big data analysis results so that they can easily understand them, and it is the process of turning data into knowledge in a way to effectively understand the results. For big data visualization, learn what big data is, learn data collection methods and various techniques of data analysis, and learn visualization methods suitable for data characteristics. A Python programming language is used to apply the learned materials for big data visualization, and structured data and unstructured data will be covered in the class. The class includes the practice of collecting, analyzing, and visualizing big data, and gives an opportunity to directly or indirectly experience of big data visualization on various topics through a project as a team. | |||||||||
| AAID015 | AI Convergence Project 1 | 3 | 6 | Major | Master | 5 | Korean | Yes | |
| This course aims the researching and developing the individual project based on the understanding of the artificial intelligence technologies. Lecturer leads the research and development project with the person-to-person lecturing and collaborative discussions. The final output of this course would be the research paper and/or the demoable project implementation. | |||||||||