For more details on the courses, please refer to the Course Catalog
| Code | Course Title | Credit | Learning Time | Division | Degree | Grade | Note | Language | Availability |
|---|---|---|---|---|---|---|---|---|---|
| AIM5029 | AI Colloquium | 1 | 2 | Major | Master/Doctor | Artificial Intelligence | - | No | |
| Thisclassprovidesbroadknowledgeaboutmanyfieldsofinformationtechnology.VarioussubjectsareselectedwhicharecurrentlyhotissuesinArtificial Intelligence andinvitedtalksaregivenabouttheselectedsubjects. | |||||||||
| AIM5033 | Multi-modal Learning | 3 | 6 | Major | Master/Doctor | Artificial Intelligence | - | No | |
| Real-word data usually contain different types of modalities. For example, images can be associated with short descriptions and in a document, some ideas are illustrated by an image even though most of the ideas are delivered by texts. This graduate course will discuss multi-modal learning techniques which integrate different types of data in a machine learning method. | |||||||||
| AIM5053 | AI and Ethics | 3 | 6 | Major | Master/Doctor | Artificial Intelligence | - | No | |
| In this course, students will analyze AI models' limitations in terms of ethics and learn how to overcome the limitations. Every technology has an intended use and unintended consequences. For example, nuclear power makes power plants and atomic bombs. AI also has this dual-use. Students will learn the problem of dual-use in AI and understand and suggest solutions. | |||||||||
| 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. | |||||||||
| 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). | |||||||||
| 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. | |||||||||
| ECE5247 | Sustainable Information Technology Seminar | 1 | 2 | Major | Master/Doctor | 1-4 | Electrical and Computer Engineering | English | Yes |
| This class provides broad knowledge about many fields of information technology. Various subjects are selected which are currently hot issues in information technology and invited talks are given about the selected subjects. | |||||||||
| ECE5756 | Real-Time System Special Topics | 3 | 6 | Major | Master/Doctor | 1-4 | Electrical and Computer Engineering | - | No |
| In this course, the issues of real-time systems are studied through the current papers. Amongst many areas of real-time systems research, a few will be selected and emphasized during the course. The research areas include languages for real-time systems, real-time system analysis, real-time OS, real-time database, real-time communication, and real-time applications. This course focuses the basic theories, design techniques, and development issues in one or two areas mentioned above. | |||||||||
| ECE5910 | Advanced Probability and Random Processes | 3 | 6 | Major | Master/Doctor | 1-5 | Electrical and Computer Engineering | - | No |
| The aim of this course is to develop a thorough understanding of the principles of random processes and knowledge of applying them to some problems in electrical engineering. First, the basic theory in probability and random process is introduced, paying particular attention to the multivariate Gaussian density function. Then, the theory of random processes and their characterization by autocorrelation and power spectral density functions is developed. The theory is then applied to the design of optimum linear systems. | |||||||||
| ECE5913 | Advanced Signal Processing | 3 | 6 | Major | Master/Doctor | 1-5 | Electrical and Computer Engineering | - | No |
| A survey of techniques for signal processing going beyond Fourier based approaches. Orthogonal transforms such as Walsh and Hadamard. Homomorphic techniques, generalized Wiener filtering, rank order filtering. Model based signal processing including autoregressive and maximum entropy, frequency-time and space-time, emphasis on algorithms and self paced projects are also covered. | |||||||||
| ECE5920 | Optimization Methods | 3 | 6 | Major | Master/Doctor | 1-5 | Electrical and Computer Engineering | English | Yes |
| Linear programming, nonlinear programming, iterative methods and dynamic programming are presented, especially as they relate to optimal control problems. Discrete and continuous optimal regulators are derived from dynamic programming approach which also leads to the Hamilton-Jacobi-Bellman Equation and the Minimum Principle. Minimum energy problems, linear tracking problems, output regulators and minimum time problems are considered. | |||||||||
| ECE5984 | Foundations of Machine Learning | 3 | 6 | Major | Master/Doctor | 1-4 | Electrical and Computer Engineering | English | Yes |
| Machine Learning is the study of how to build computer systems that learn from experience. This course will give an overview of many models and algorithms used in modern machine learning, including generalized linear models, multi-layer neural networks, support vector machines, Bayesian belief networks, clustering, and reinforcement learning. | |||||||||
| ESW4001 | Virtual Reality Theory | 3 | 6 | Major | Bachelor/Master | Computer Science and Engineering | - | No | |
| Virtual reality is an interdisciplinary next-generation medium that fuses many different areas upon computer science and engineering. This course focuses on the technological aspects of virtual reality, and deals with the fundamentals of theories, hardware/software, and its applications. The major subjects include virtual reality systems, the basics of computer graphics and stereoscopic rendering, vision/auditory/haptic perception, 3D interaction and practical implementation techniques. | |||||||||