For more details on the courses, please refer to the Course Catalog
| Code | Course Title | Credit | Learning Time | Division | Degree | Grade | Note | Language | Availability |
|---|---|---|---|---|---|---|---|---|---|
| AIM5051 | AI Business Platform | 3 | 6 | Major | Master/Doctor | Artificial Intelligence | - | No | |
| This course aims to study diverse business models based on AI and to study platform for constructing AI business ecosystem. In other words, this course studies the system (environment) where various complementary interactions can occur in the ecosystem of business, service, or technology through AI technology development. Through this course, a platform in which AI developers, analysts, providers, and users can coexist in a virtuous circle can be constructed. | |||||||||
| AIM5052 | Manufacturing Bigdata Analysis | 3 | 6 | Major | Master/Doctor | Artificial Intelligence | - | No | |
| This course is aim to understand the big data characteristics of the manufacturing domain and to learn the big data analytics capability in the manufacturing field. First, we will learn about the importance of manufacturing big data by approaching from a business point of view, and then discuss various computing technologies and software platforms which can deal with manufacturing big data. This course deals with the understanding of statistical language R and basic statistics. It also analyzes how to control big data by explaining efficient data extraction, data analytics and data visualization. It also covers logistic regression, LDA, clustering algorithms, time-series analysis, SVM/KNN. We will explore techniques for handling big data using Hadoop or Spark, which are recently attracting attention as a platform for processing big data. We will learn about production process in manufacturing and look at production management and quality control based on big data analytics. | |||||||||
| AIM5055 | Bayesian learning | 3 | 6 | Major | Master/Doctor | Artificial Intelligence | - | No | |
| In this course, students will learn Bayesian learning. Bayesian statistical methods help estimate probabilities and make decisions under uncertainty. And Bayesian Deep Learning, which combines the benefits of modern deep learning methods and modern Bayesian statistical methods, is emerging. Students will learn Bayesian learning at first and learn the Bayesian Deep Learning in more detail. | |||||||||
| AIM5056 | Machine learning with Graphs | 3 | 6 | Major | Master/Doctor | Artificial Intelligence | English | Yes | |
| Machine learning with graphs is a quickly growing subfield of machine learning that seeks to apply machine learning methods to graph-structured data. Applications of machine learning on graphs include drug design, user profiling, and friendship recommendation in social networks. This course will provide an introduction to graph representation learning, including matrix factorization-based methods, random-walk based algorithms, and graph neural networks. During the course, we will study both the theoretical motivations and practical applications of these methods. | |||||||||
| AIM5057 | Knowledge Graph-based Methods | 3 | 6 | Major | Master/Doctor | Artificial Intelligence | - | No | |
| This course is to learn foundations, techniques, and algorithms for building and leveraging knowledge graphs. Students will study the theory and applications of the techniques needed to build and query massive knowledge graphs. Topics include crawling web sites, wrapper learning, information extraction, source alignment, string matching, entity linking, graph databases, querying knowledge graphs, data cleaning, Semantic Web, linked data, graph analytics, and intellectual property. | |||||||||
| AIM5058 | Variational Inference | 3 | 6 | Major | Master/Doctor | 1-4 | Artificial Intelligence | Korean | Yes |
| The goal of inference problem is to find a structure hidden in the data. This can often be achieved by getting the posterior probability distribution which is intractable in many cases. Variational inference (VI) solve this by casting inference problem as an optimization. In this course, we explore VI and stochastic VI after learning basic probability theory and Monte-Carlo methods. The connection between VI and VAE is also provided. | |||||||||
| AIM5064 | Special topics in visual computing | 3 | 6 | Major | Master/Doctor | 1-4 | Artificial Intelligence | Korean | Yes |
| This is a graduate seminar course in visual computing. We will survey and discuss the recent research papers in computer vision area, such as image recogniaion, reconstruction, 3D vision, simulation, generative models, etc. Throughout this course, students get familiar with the recent innovations in computer vision area and identify open questions and new research directions in this field. | |||||||||
| AIM5065 | OPEN AI NETWORKING | 3 | 6 | Major | Master/Doctor | 1-4 | Artificial Intelligence | English | Yes |
| Mobile/wireless networks are going through a new AI revolution triggered by the challenges of hyper-connectivity, hyper-low latency communication, and massive data orchestration for enormous connected objects. As such, they are one of the most active research areas in Beyond 5G and 6G in terms of growth and innovation. The “AI and 5G/6G” course covers basic knowledge of 5G/6G mobile networks and available AI technologies for improved network performance and efficient management of resources. In particular, the course is split in three parts, where the first part discusses basic 5G architecture and new technologies that are shaping 6G architecture, such as cloud-native computing, AI-native communication, and deterministic networking. Second part covers the state-of-the-art Deep Learning (DL) approaches that are relevant for 5G/6G mobile networks, like recurrent models, generative adversarial networks, transformer networks, and deep reinforcement learning. Third part presents the latest case studies of AI based dynamic orchestration of network behavior by using parameters like traffic variation, localization, mobility, and user context. At the end of the course, the student will have a comprehensive vision of 5G/6G mobile networks and relevant state-of-the-art AI technologies that open up numerous industrial, management, and research opportunities. | |||||||||
| AIM5065 | OPEN AI NETWORKING | 3 | 6 | Major | Master/Doctor | 1-4 | Artificial Intelligence | English | Yes |
| Mobile/wireless networks are going through a new AI revolution triggered by the challenges of hyper-connectivity, hyper-low latency communication, and massive data orchestration for enormous connected objects. As such, they are one of the most active research areas in Beyond 5G and 6G in terms of growth and innovation. The “AI and 5G/6G” course covers basic knowledge of 5G/6G mobile networks and available AI technologies for improved network performance and efficient management of resources. In particular, the course is split in three parts, where the first part discusses basic 5G architecture and new technologies that are shaping 6G architecture, such as cloud-native computing, AI-native communication, and deterministic networking. Second part covers the state-of-the-art Deep Learning (DL) approaches that are relevant for 5G/6G mobile networks, like recurrent models, generative adversarial networks, transformer networks, and deep reinforcement learning. Third part presents the latest case studies of AI based dynamic orchestration of network behavior by using parameters like traffic variation, localization, mobility, and user context. At the end of the course, the student will have a comprehensive vision of 5G/6G mobile networks and relevant state-of-the-art AI technologies that open up numerous industrial, management, and research opportunities. | |||||||||
| 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. | |||||||||
| 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. | |||||||||
| DMC5005 | Writing of IT Technical Papers in English | 2 | 4 | Major | Master/Doctor | 1-4 | English | Yes | |
| This course applies to technical writing of papers and documents in English for students in electronic, electrical, and computer engineering. It is utmost important to be able to write technical papers and documents precisely and tersely in English according to a certain rule and formality, and it is a crucial part of doing research. Through learning technical writing in English and performing such writings, students can improve their written communication skill with other people for technical matters. | |||||||||
| DMC5006 | Humanities and Technology | 1 | 2 | Major | Master/Doctor | Korean | Yes | ||
| Specialized humanities course for students majoring in electronic, electrical, and computer engineering. It is a humanistic journey for developing human-oriented technology that let students feel from the head to the chest and from the chest to the toe rather than studying the liberal arts. Through human-oriented thinking and case study, it targets for broadening the horizons beyond the technology-oriented problem solving and for recognizing that the ultimate goal of product development and technology development is for human. The lectures are given by professors in College of Liberal Arts, School of Business, and College of Information and Communication Engineering. | |||||||||
| DMC5007 | On-device Deep Learning | 3 | 6 | Major | Master/Doctor | 1-4 | - | No | |
| This course pursues in-depth study on deep neural network (DNN) compression techniques for on-device deep learning, which allows smartpones and IoT devices to execute DNN applications. The detailed topics include DNN pruning, low-precision bit quantization, and neural network architecture search (NAS). | |||||||||
| DMC5007 | On-device Deep Learning | 3 | 6 | Major | Master/Doctor | 1-4 | Digital Media Communication | - | No |
| This course pursues in-depth study on deep neural network (DNN) compression techniques for on-device deep learning, which allows smartpones and IoT devices to execute DNN applications. The detailed topics include DNN pruning, low-precision bit quantization, and neural network architecture search (NAS). | |||||||||