For more details on the courses, please refer to the Course Catalog
| Code | Course Title | Credit | Learning Time | Division | Degree | Grade | Note | Language | Availability |
|---|---|---|---|---|---|---|---|---|---|
| AIM4004 | Intro to AI Agent | 3 | 6 | Major | Bachelor/Master | 1-8 | Artificial Intelligence | - | No |
| This course aims to understand the technical foundations upon which modern AI services, such as ChatGPT, are built and operate. Beyond the working principles of simple models, it provides a broad overview of the full technical stack required to implement commercial-grade AI services. To this end, the course begins with the latest LLM development paradigms, including Transformer-based model structures, pre-training, fine-tuning, and instruction tuning. It then progressively covers key elements from the perspective of implementing actual agent systems, such as prompt engineering, Retrieval-Augmented Generation (RAG), agent planning & reasoning, multi-agent collaboration, and tool use. Furthermore, by analyzing recent research papers and results, the course aims to help students grasp rapidly changing technology trends and cultivate the ability to design and build sophisticated AI systems based on this knowledge. | |||||||||
| AIM5001 | Theories of Artificial Intelligence | 3 | 6 | Major | Master/Doctor | Artificial Intelligence | Korean | Yes | |
| In this course students will learn the fundamental algorithms of Aritificial Intelligence including the problem solving techniques, search algorithms, logical agents, knowledge representation, inference, and planning. After taking the course, students are expected to implement the algorithms using computer programming languages. | |||||||||
| AIM5004 | Deep Neural Networks | 3 | 6 | Major | Master/Doctor | Artificial Intelligence | - | No | |
| In this class, we will cover the following state-of-the-art deep learning techniques such as linear classification, feedforward deep neural networks (DNNs), various regularization and optimization for DNNs, convolutional neural networks (CNNs), recurrent neural networks (RNN), attention mechanism, generative deep models (VAE, GAN), visualization and explanation. | |||||||||
| AIM5025 | Intelligent Robot and System | 3 | 6 | Major | Master/Doctor | 1-4 | Artificial Intelligence | - | No |
| Inordertouserobotsveryefficiently,robotsarerequestedtobeabletoperformalltasksashumanscan.Thiscoursediscussesthetechniqueofsensoranditsapplicationinordertomakerobotsperformtasksintelligently. | |||||||||
| AIM5026 | Introduction to Robotic Intelligence | 3 | 6 | Major | Master/Doctor | Artificial Intelligence | - | No | |
| Robot is defined as an intelligent system connecting sensors and actuators. As an intelligent system, robot is to play a key role for providing necessary services to human by automatically carrying out tasks requiring navigation and manipulation. To this end, robot needs to recognize objects and understand surroundings while reasoning and planning the behaviors necessary for carrying out tasks. Especially, it is essential for robot to be able to obtain its capabilities of recognition and understanding of environments as well as of reasoning and planning of behaviors by learning. This course deals with the fundamentals of robot intelligence on how robot learns for the recognition and understanding of environments as well as for the reasoning and planning of behaviors associated with manipulation and navigation. | |||||||||
| AIM5064 | Special topics in visual computing | 3 | 6 | Major | Master/Doctor | 1-4 | Artificial Intelligence | - | No |
| 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 | - | No |
| 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. | |||||||||
| AIM5069 | Retrieval-Augmented Generation | 3 | 6 | Major | Master/Doctor | 1-8 | Artificial Intelligence | Korean | Yes |
| Retrieval-Augmented Generation (RAG) is an AI technique in which a Large Language Model (LLM) first retrieves relevant information from an external database or document collection before generating an answer based on that information. This approach addresses key issues of LLMs, such as hallucinations and lack of up-to-date knowledge. In this course, we will 1) learn foundational information-retrieval technologies such as BM25 and DPR, and 2) study how these retrieval techniques can be integrated with LLMs to improve the quality of generated outputs. | |||||||||
| AIM5070 | Spoken Language Processing | 3 | 6 | Major | Master/Doctor | 1-8 | Artificial Intelligence | Korean | Yes |
| This course provides how recent AI developments are applied to speech processing, building on conceptual and mathematical foundations of speech signals. Core topics are speech recognition (STT) and speech synthesis (TTS), including essential natural language processing techniques required for speech AI. The course also explores how speech AI is evolving in the era of large language models (LLMs). | |||||||||
| 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. | |||||||||
| DED4001 | Artificial Intelligence for Display Engineering | 3 | 6 | Major | Bachelor/Master | 1-4 | Display Engineering | Korean | Yes |
| ‘Artificial Intelligence for Display Engineering’ is a course that introduces advanced artificial intelligence (AI) techniques for display engineering. It covers both conventional display manufacturing and inspection, including OLED and MicroLED panels, and emerging display technologies such as AR, VR, and XR systems, holographic displays, and light-field displays. The course presents key AI concepts used in display image processing, 3D scene understanding, and optical modeling, ranging from convolutional neural networks and vision transformers to Implicit Neural Representation (INR) methods such as Neural Radiance Fields (NeRF). Students learn the basic principles of NeRF, 3D Gaussian Splatting (3DGS), and inverse rendering, which are essential for geometric reconstruction, high-quality rendering, and distortion correction in immersive display environments. In addition, the course introduces AI techniques used in real manufacturing and inspection workflows, including defect image analysis, anomaly detection, defect correction, super-resolution, and data-driven optical property prediction. Through practical analysis of real inspection data, students gain experience applying AI to both traditional and next-generation display systems. The course aims to cultivate interdisciplinary talent capable of contributing to intelligent display manufacturing, next-generation display design, and AI-based rendering and optical correction. | |||||||||
| 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). | |||||||||
| ECE4223 | Semiconductor Process Technology | 3 | 6 | Major | Bachelor/Master | 1-4 | 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. | |||||||||
| ECE4233 | Simulation Engineering of Electric Power Systems | 3 | 6 | Major | Bachelor/Master | 1-4 | Korean | Yes | |
| The objective of this lecture is to present methods of power system simulation, particularly with the aid of a personal computer, in sufficient depth to give the student the basic technique at the graduate level. Main subjects are steady-state & transient power system simulation, fault modeling technique, FACT(flexible alternating current transmission) simulation and the usage of EMTP, ATP, PSCAD/EMTDC. | |||||||||
| ECE4237 | Robotics | 3 | 6 | Major | Bachelor/Master | 1-4 | Korean | Yes | |
| 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. | |||||||||