For more details on the courses, please refer to the Course Catalog
| Code | Course Title | Credit | Learning Time | Division | Degree | Grade | Note | Language | Availability |
|---|---|---|---|---|---|---|---|---|---|
| ECE4282 | AI Integrated Circuits Design | 3 | 6 | Major | Bachelor/Master | 1-4 | Korean | Yes | |
| In this course, AI Integrated Circuits are covered. Especially, recent research trends of the Processor In Memory (PIM) with the good energy efficiency will be dealt and design practice using the EDA tools will be done. | |||||||||
| ECE4282 | AI Integrated Circuits Design | 3 | 6 | Major | Bachelor/Master | 1-4 | Electrical and Computer Engineering | Korean | Yes |
| In this course, AI Integrated Circuits are covered. Especially, recent research trends of the Processor In Memory (PIM) with the good energy efficiency will be dealt and design practice using the EDA tools will be done. | |||||||||
| ECE4283 | Intelligent System Integrated Circuit Design | 3 | 6 | Major | Bachelor/Master | 1-4 | Korean | Yes | |
| In this course, intelligent system integrated circuits are covered. Especially, recent research trends of the intelligent power management circuit, AI based sensor signal processing circuits will be dealt and design practice using the EDA tools will be done. | |||||||||
| ECE4283 | Intelligent System Integrated Circuit Design | 3 | 6 | Major | Bachelor/Master | 1-4 | Electrical and Computer Engineering | Korean | Yes |
| In this course, intelligent system integrated circuits are covered. Especially, recent research trends of the intelligent power management circuit, AI based sensor signal processing circuits will be dealt and design practice using the EDA tools will be done. | |||||||||
| ECE4284 | Automotive Embedded Software | 3 | 6 | Major | Bachelor/Master | English | Yes | ||
| This course describes automotive embedded software and automotive software standard architecture, AUTOSAR Classic. The aim is to understand the main characteristics of automotive embedded software and to get hands-on experience developing software based on AUTOSAR Classic. | |||||||||
| ECE4285 | Theory and coding of generative AI | 3 | 6 | Major | Bachelor/Master | Korean | Yes | ||
| Along with the rapid advancement of AI through deep learning in recent years, generative artificial intelligence (AI) models like Stable Diffusion and ChatGPT are rapidly infiltrating various industries, thus being expected to lead the Fourth Industrial Revolution. In this educational program, the goal is to understand the theory and principles behind these deep learning and generative AI models. Students will develop practical skills necessary for utilizing these powerful technologies through computer programming practical applications and projects. Specifically, this course covers: (1) Understanding and hand-on coding of deep learning models such as CNN, YOLO, Semantic Segmentation, and GANs. (2) Learning the mathematical explanation and practical applications of Diffusion models, including understanding the principles of image generation through the analysis of open-source code of Stable Diffusion. (3) Understanding language generation models like Seq2Seq and Transformer architecture used in ChatGPT, and practical applications in natural language processing and related fields. Through this course, students will gain the knowledge of current and future AI technologies and acquire the skills needed to address real-world problems. | |||||||||
| ECE4286 | Artificial intelligence semiconductor memory device | 3 | 6 | Major | Bachelor/Master | - | No | ||
| Artificial intelligence algorithms based on deep neural networks (DNN: Deep Neural Networks) are demonstrating performance that is similar to or exceeds that of humans in many complex cognitive tasks. However, the energy efficiency of current Neumann computing systems implementing deep neural network algorithms is very low compared to the human brain. To solve this problem, neuromorphic hardware, which consists of densely connected parallel neurons and synapses that mimics the brain neural network structure, was proposed. The objective of this lecture is to deliver a driving principles of neuromorphic hardware and memristor memories, which expresses the weight of synapses in neuromorphic hardware. It will be introduced the operating principles of various memristors, including floating gate memory type, resistive memory type, phase change memory type, magnetic random access memory type, and ferroelectric memory type. | |||||||||
| ECE5237 | Master＇s Research Problem I | 3 | 6 | Major | Master | 1-4 | Korean,English | Yes | |
| Performs research on a topic assigned by his or her advisor for his Master's degree. | |||||||||
| ECE5237 | Master＇s Research Problem I | 3 | 6 | Major | Master | 1-4 | Electrical and Computer Engineering | Korean,English | Yes |
| Performs research on a topic assigned by his or her advisor for his Master's degree. | |||||||||
| ECE5246 | Master Research on Engineering Solutions to Societal Challenges | 2 | 4 | Major | Master | 1-4 | English | Yes | |
| This is a graduate-level course for students pursuing a master degree. In this course, students identify an engineering research problem on societal challenges and perform independent research on the selected problem during the semester under the guidance of their advisors. | |||||||||
| ECE5247 | Sustainable Information Technology Seminar | 1 | 2 | Major | Master/Doctor | 1-4 | 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. | |||||||||
| 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. | |||||||||
| ECE5301 | Advanced Information Theory | 3 | 6 | Major | Master/Doctor | 1-4 | - | No | |
| The aims of this course are to introduce the principles and applications of information theory. This course will study how information is measured in terms of probability and entropy. A subset of the following is covered: entropy and information; theoretical limits of lossless data compression and practical algorithms; communication in the presence of noise - channel coding, Shannon's theorem on the properties of the entropy function and some of the best known data encryption techniques will also be presented. | |||||||||
| ECE5301 | Advanced Information Theory | 3 | 6 | Major | Master/Doctor | 1-4 | Electrical and Computer Engineering | - | No |
| The aims of this course are to introduce the principles and applications of information theory. This course will study how information is measured in terms of probability and entropy. A subset of the following is covered: entropy and information; theoretical limits of lossless data compression and practical algorithms; communication in the presence of noise - channel coding, Shannon's theorem on the properties of the entropy function and some of the best known data encryption techniques will also be presented. | |||||||||
| ECE5302 | Pattern Recognition | 3 | 6 | Major | Master/Doctor | 1-4 | - | No | |
| This lessen discusses basic technologies on processing and recognition of digital image patterns. Main subjects are statistical pattern recognition, supervised learning, linear discrimination functions, unsupervised learning, syntactic pattern recognition, parsing and grammers, graphical syntactic pattern recognition, grammatical inference, neural pattern recognition, and so on | |||||||||