For more details on the courses, please refer to the Course Catalog
| Code | Course Title | Credit | Learning Time | Division | Degree | Grade | Note | Language | Availability |
|---|---|---|---|---|---|---|---|---|---|
| COM3029 | Introduction to Cloud Computing | 3 | 6 | Major | Bachelor | 3-4 | Korean | Yes | |
| Understand the core technologies of cloud computing, and see how those technologies are actually used across the industry today. In addition, various application methods are explored through service cases where related technologies are applied in real life. This course provides a clear definition of key elements of cloud computing, from the concept of cloud computing to models and technology architectures. This course delivers the knowledge that everyone needs, from students who are interested in cloud computing to students who want to gain professional knowledge that can be applied directly to practice. | |||||||||
| COM3030 | Introduction to Mobile Programming | 3 | 6 | Major | Bachelor | 3-4 | - | No | |
| Understand the core technologies of mobile computing, and see how they are actually used in industry today. Students will learn the basic concepts and principles of mobile wireless communication, application technology, development platform, and development tools related to mobile computing environment. Students will gain knowledge of designing, designing and developing mobile applications through programming practice. | |||||||||
| COM3033 | Big Data Visualization | 3 | 6 | Major | Bachelor | 3-4 | - | No | |
| In this subject, learners learn the art of visualizing big data effectively. Implement and validate visualization techniques using Python languages based on a variety of data analysis types. | |||||||||
| COM3034 | Intelligent Vision | 3 | 6 | Major | Bachelor | - | No | ||
| In this lecture, we learn about the convergence of intelligent vision theory and industrial products. At the beginning of the lecture, we learn about computer vision basic theories including image data basics, template classes, pixel processing, image classification, and scene understanding. In the second half of the lecture, the theories related to deep learning model training and inference are learned. Further, we practice the deep learning model inference process by inputting various deep learning models developed with the latest frameworks such as Tensorflow, Pytorch, and Darknet into the OpenCV DNN module. | |||||||||
| COM3035 | Introduction to Data Science and Analytics | 3 | 6 | Major | Bachelor | 3-4 | Korean | Yes | |
| This course teaches the basics of data science and analytics. The topics covered in this course are fundamentals of data science, basic data analysis, data analysis frameworks, tools and techniques to learn data analysis, cloud in data analysis, data analysis systems, and trending technologies in data science. | |||||||||
| COM3036 | Software design | 3 | 6 | Major | Bachelor | Korean | Yes | ||
| In this lecture, we learn programming techniques using object-oriented and generalized programming languages so that they can be applied in practice. Understand and utilize the basic concepts of object-oriented language, objects, classes, polymorphisms, inheritance, etc., and cultivate the ability to solve problems using object-oriented language. We also learn how to design software that operates in various environments through generalized programming techniques. | |||||||||
| COM3037 | Analysis of Global Artificial Intelligence Education Trend | 3 | 6 | Major | Bachelor | Korean | Yes | ||
| This subject helps students have the ability to explore effective educational strategies related to artificial intelligence(AI) technology through analysis of global trends on the level and development of AI technology. By looking at AI education policies and the latest AI education research trends in major foreign countries, students aim to have a broad understanding of the global AI education environment and grow into competitive AI education experts. Through this class, students will have the opportunity to compare and analyze various AI education policies and strategies in the global educational environments and discuss the potential for future development, and will be able to develop strategies applicable to the field of AI education in Korea and apply them to the actual education field. | |||||||||
| ERP4001 | Creative Group Study | 3 | 6 | Major | Bachelor/Master | - | No | ||
| This course cultivates and supports research partnerships between our undergraduates and faculty. It offers the chance to work on cutting edge research—whether you join established research projects or pursue your own ideas. Undergraduates participate in each phase of standard research activity: developing research plans, writing proposals, conducting research, analyzing data and presenting research results in oral and written form. Projects can last for an entire semester, and many continue for a year or more. SKKU students use their CGS(Creative Group Study) experiences to become familiar with the faculty, learn about potential majors, and investigate areas of interest. They gain practical skills and knowledge they eventually apply to careers after graduation or as graduate students. | |||||||||
| ISS3222 | Introduction to Machine Learning | 3 | 6 | Major | Bachelor | - | No | ||
| Covers fundamental concepts for intelligent systems that autonomously learn to perform a task and improve with experience, including problem formulations (e.g., selecting input features and outputs) and learning frameworks (e.g., supervised vs. unsupervised), standard models, methods, computational tools, algorithms and modern techniques, as well as methodologies to evaluate learning ability and to automatically select optimal models. Applications to areas such as computer vision (e.g., characte r and digit recognition), natural language processing (e.g., spam filtering) and robotics (e.g., navigating complex environments) will motivate the coursework and material. | |||||||||
| MAE3029 | Mathmatics in Artificial Intelligence | 3 | 6 | Major | Bachelor | Mathematics Education | Korean | Yes | |
| In this course, we first deal with the mathematical contents that are helpful in studying machine learning, and based on this, we introduce the four elements of machine learning. Mathematics contents covered are linear algebra, analytic geometry, matrix decomposition, vector calculus, and probability and distribution. In addition, theoretical optimization contents and optimization methodology dealing with techniques such as gradient descent are also studied. Based on these mathematical contents, regression, dimensionality reduction, density estimation, and classification are introduced, and further, neural networks and deep learning are briefly covered. | |||||||||