For more details on the courses, please refer to the Course Catalog
| Code | Course Title | Credit | Learning Time | Division | Degree | Grade | Note | Language | Availability |
|---|---|---|---|---|---|---|---|---|---|
| AIM5058 | Variational Inference | 3 | 6 | Major | Master/Doctor | 1-4 | 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. | |||||||||
| AIM5059 | AI Master Independent Research 2 | 3 | 6 | Major | Master/Doctor | Korean | Yes | ||
| In this course, students identify a research problem and perform independent research on the selected problem during the semester under the guidance of their advisors. | |||||||||
| AIM5060 | AI PhD Independent Research2 | 3 | 6 | Major | Master/Doctor | Korean | Yes | ||
| In this course, students identify a research problem and perform independent research on the selected problem during the semester under the guidance of their advisors. | |||||||||
| AIM5061 | AI PhD Independent Research3 | 3 | 6 | Major | Master/Doctor | Korean | Yes | ||
| In this course, students identify a research problem and perform independent research on the selected problem during the semester under the guidance of their advisors. | |||||||||
| AIM5062 | Advanced Deep Personalization System | 3 | 6 | Major | Master/Doctor | 1-4 | - | No | |
| Recently, personalization systems using deep neural networks have been actively studied. For search, pre-trained language models, such as BERT and GPT, effectively improve the vocabulary mismatch problem. Besides, various neural networks have been actively used to recommendation systems. For sequential recommendations, recurrent neural networks (RNN) and transformer and graph neural networks (GNN) are commonly utilized. For conversational recommendation systems, various attempts have been made to combine recommendation models with DNN-based conversation model. In this course, we investigate and discuss the state-of-the-art papers on recent research trends in personalizaion systems including search and recommendation, published in top-tier conferences (WWW, SIGIR, KDD, ICDM, CIKM, WSDM, NeurIPS, ICLR, ICML, AAAI, IJCAI, and RecSys). Furthermore, I encourage the students to write the technical reports that improve and overcome the limitations of existing studies. | |||||||||
| AIM5064 | Special topics in visual computing | 3 | 6 | Major | Master/Doctor | 1-4 | 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 | 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. | |||||||||
| AIM5066 | Large Language Models | 3 | 6 | Major | Master/Doctor | 1-4 | Korean | Yes | |
| Large language models (LLMs) have actively undergone a revolutionary transformation in the field of natural language processing (NLP). Serving as the cornerstone for cutting-edge systems, these models have become pervasive in addressing a diverse array of tasks related to natural language understanding and generation. While LLMs exhibit unprecedented potential and capabilities, they also give rise to new challenges, particularly in the realms of ethics and scalability. This course is designed to delve into the forefront of research, focusing on pre-trained language models. We will explore their technical underpinnings, including models such as BERT, GPT, T5, mixture-of-expert models, and retrieval-based models. Additionally, we will investigate emerging capabilities like knowledge integration, reasoning abilities, few-shot learning, and in-context learning. The curriculum will extend to cover aspects such as fine-tuning and adaptation, and the crucial dimensions of security and ethics. We will thoroughly examine each topic and engage in in-depth discussions of influential papers. Students will actively participate by routinely reading and presenting research papers. | |||||||||
| BPC5014 | IQB Colloquim1 | 3 | 6 | Major | Master/Doctor | 1-8 | Biophysics | English | Yes |
| The main objective of the course is for IQB students to learn research topics in multiple areas, widen their insights, and consequently elevate their research. This course is composed of weekly seminars provided by speakers from SKKU and others with introductory to and recent publications in their multidisciplinary areas. | |||||||||
| 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. | |||||||||
| ECE4223 | Semiconductor Process Technology | 3 | 6 | Major | Bachelor/Master | 1-4 | Electrical and Computer Engineering | English | Yes |
| This course helps to understand the overall semiconductor processes by introducing the theory and the application of unit processes; photolithography, photo-mask, dry-etch, cleaning, chemical-mechanical polishing(CMP), diffusion and thin film, and module processes; transistor, isolation, capacitor, interconnection. This also suggests the direction of process technologies for the future generations. | |||||||||
| ECE4237 | Robotics | 3 | 6 | Major | Bachelor/Master | 1-4 | Electrical and Computer Engineering | - | No |
| This course discusses the kinematics and the dynamics of manipulators. The path planning of each joint and some control algorithms of manipulators are also discussed. | |||||||||
| ECE4238 | Linear Systems | 3 | 6 | Major | Bachelor/Master | 1-4 | Electrical and Computer Engineering | English | Yes |
| Methods of analysis for continuous and discrete-time linear systems. Convolution, classical solution of dynamic equations, transforms and matrices are reviewed. Emphasis is on the concept of state space. Linear spaces, concept of state, modes, controllability, observability, state transition matrix, state variable feedback, compensation, decoupling are treated. | |||||||||
| ECE4249 | Computer Vision | 3 | 6 | Major | Bachelor/Master | 1-4 | Electrical and Computer Engineering | Korean | Yes |
| This course focuses in the study of theories for image analysis. The first part consists of Image formulation model, early processing, boundary detection, region growing and segmentation, motion detection, merging and introduction of morphology. The second part, we cover basic concepts of statistical model, dis- criminant function, decision boundary and rules and neural network for visual pattern recognition. | |||||||||
| ECE4270 | Image Processing | 3 | 6 | Major | Bachelor/Master | 1-4 | Electrical and Computer Engineering | English | Yes |
| This class provides fundamental knowledge for acquisition, processing, display of digital image signals by studying such topics as mathematical modeling of image signal, sampling, spatial and temporal resolution, human visual system, quantization theory, basic 2D signal processing, 2D transform, frequency analysis, filtering, image enhancement, color space, color processing, and compression and reconstruction. Selected practical applications are analysed for better understanding of such techniques. | |||||||||