For more details on the courses, please refer to the Course Catalog
| Code | Course Title | Credit | Learning Time | Division | Degree | Grade | Note | Language | Availability |
|---|---|---|---|---|---|---|---|---|---|
| AIM4001 | Advanced Big Data Analytics | 3 | 6 | Major | Bachelor/Master | Artificial Intelligence | - | No | |
| This course introduces fundamental data mining and machine learning techniques for big data analytics. The emphasis in the course will be learning key techniques that are required to extract meaningful information from big data, and developing scalable data mining algorithms for big data analytics. The first half of the course will cover various supervised and unsupervised machine learning methods (theoretical analysis of the methods and their practical applications), and the last half of the course will focus on scalable graph mining techniques with special emphasis on analyzing large-scale social networks. There will be one midterm, three assignments, and the final project where students will be expected to develop scalable algorithms for collecting and analyzing big data. | |||||||||
| 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 | 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. | |||||||||
| 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. | |||||||||
| 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 | - | No | |
| 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 | - | 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 | 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. | |||||||||
| ECE4245 | Automation and Design of Electric Power System | 3 | 6 | Major | Bachelor/Master | 1-4 | Korean | Yes | |
| In many coutries, the electric supply industry is undergoing a significant restructuring that has only just begun. Fully competitive markets of electric energy are fairly recent phenomena, and the existing competitive markets have not experienced significant of resource shortages yet. This course deals with the basic principles of power system automation from the point view of the central control facilities under competitive environment. And it discusses central computers used to process power system economic operating data, present vital data to an operator, and allow the operator to implement commands to equipment field. Also it contains the economic load dispatch, the Gauss-Seidel, Newton-Raphson, and decoupled method to iteratively calculate network power flow. | |||||||||
| ECE4246 | Digital Control | 3 | 6 | Major | Bachelor/Master | 1-4 | - | No | |
| Many industrial control systems include digital computers as an integral part of their operation. Recent trends toward digital control of dynamic systems, rather than analog control, is mainly due to the recent revolutionary advances in digital computers and to advantages found in working with digital signals rather than continuous-time signals. Also, the availability of low-cost microprocessors and microcomputers established a new trend for even small-scale control systems to include digital computers to obtain optimal performance. The main purpose of this course is to present a comprehensive treatment of the analysis and design of discrete-time control systems. In particular, this course provides clear and easy-to-understand explanations for concepts involved in the study of discre-time control systems. | |||||||||