For more details on the courses, please refer to the Course Catalog
| Code | Course Title | Credit | Learning Time | Division | Degree | Grade | Note | Language | Availability |
|---|---|---|---|---|---|---|---|---|---|
| ESW5024 | Advanced Data Analysis | 3 | 6 | Major | Master/Doctor | Computer Science and Engineering | English | Yes | |
| This class is to explore the most common methods used in the field of data analysis and statistical modeling to analyze real-world data, i.e., data summaries, visualization, prediction, and as tools for scientific inference and causal data analysis. The course deals with benchmark data that displays non-linear patterns, frequency data, count data, and longitudinal data. Assuming that students are familiar with basic probability and mathematical statistics, the course covers several topics related to VC theory, convergence, point and interval estimation, maximum likelihood, hypothesis testing, data reduction, Bayesian inference, nonparametric statistics, and bootstrap resampling, dependent data analysis, and causal inference. By completing the course, students with a data analysis problem will be able to select the appropriate statistical/analytical methods to critically evaluate the resulting statistical models, and report the results. The course's primary goal is to familiarize graduate students with the modern methods of data analysis and help them to choose the right method for the research job at hand (rather than distorting the problem to fit the methods you happen to know). This is crucial for having high-quality research results. | |||||||||
| ESW5025 | Infra Networks and Security | 3 | 6 | Major | Master/Doctor | Computer Science and Engineering | - | No | |
| This course introduces technologies and standards of infrastructure networks and security. This course explains the technologies of networks and applications in the network infrastructure (e.g., cloud, Internet of Things, and vehicular networks), and also the technologies of security and privacy in this infrastructure. The contents of this course are as follows. - Week 1: Introduction to Infra Networks and Security - Week 2: Internet and Computer Networks - Week 3: Software-Defined Networking (SDN) - Week 4: Network Functions Virtualization (NFV) - Week 5: OpenStack-Based Cloud Systems - Week 6: Cloud-Based Security Service Systems - Week 7: YANG-Based Data Modeling - Week 8: Term Project Proposal - Week 9: NETCONF-Based Network Management - Week 10: Internet-of-Things (IoT) Networking - Week 11: IoT Applications - Week 12: IoT Security - Week 13: Vehicular Networking - Week 14: Vehicular Network Applications and Security - Week 15: Term Project Presentation This course will be delivered in the format of a flipped class. It has four homeworks and one term project. | |||||||||
| ESW5026 | Advanced Operating Systems Design | 3 | 6 | Major | Master/Doctor | Computer Science and Engineering | - | No | |
| This course covers the design and implementation of the principal operating systems components, such as process management, memory management and file systems. Specifically, we will examine the technological advancement in operating systems design by dissecting the historically influential operating systems. In addition, we will explore the future directions of operating systems by investigating the key issues in the cutting-edge hardware and software technology. | |||||||||
| ESW5027 | Advanced Computer Architecture Design | 3 | 6 | Major | Master/Doctor | Computer Science and Engineering | - | No | |
| This course purses in-depth study on advanced computer architecture. The class topic includes computer performance measurement, advanced cache design, instruction-level parallelism, multi-core processor, virtual memory, and storage systems. | |||||||||
| ESW5028 | Principles of Database Systems | 3 | 6 | Major | Master/Doctor | Computer Science and Engineering | - | No | |
| This course covers the architecture and principles of modern database systems, including relational databases and key-value stores. Major topics include database system architecture, storage, index, query optimization, and transaction management, with a focus on the new design ideas for flash storage and non-volatile memory. | |||||||||
| ESW5029 | Advanced Topics in Software Engineering | 3 | 6 | Major | Master/Doctor | Computer Science and Engineering | Korean | Yes | |
| This course aims to provide an in-depth understanding of a variety of issues, particularly important issues in the software development process. For example, it enables in-depth learning by paying attention to one issue each semester among various issues such as architecture design, requirements engineering, software design, software testing, debugging, and project management. This course is an advanced course of the software engineering at the undergraduate. | |||||||||
| ESW5031 | Advanced Topics in Computer Architecture Design | 3 | 6 | Major | Master/Doctor | Computer Science and Engineering | Korean | Yes | |
| Students will study the newest technologies and research trends on comptuer CPUs, GPU, and domain-specific accelerators for increasing their performance, efficiency, scalability, and security. The class topics include memory and cache structure, interconnection networks for multi- and many-core processors, virtualization techniques, GPU architectures, neural processing units, and other advanced computer architecture topics. | |||||||||
| ESW5032 | Medical System Security | 3 | 6 | Major | Master/Doctor | Computer Science and Engineering | - | No | |
| Medical systems have become one of the application field of ICT and the security issues are important for safe medical services. In this course, we learn the vulnerabilities of the medical networks, systems, database, and contents, then discuss how to protect the threats and attacks exploiting the weak points. The fundamental security techniques and special ones for medical security will be dealt in detail. | |||||||||
| ESW5034 | Machine Learning Security and Robustness | 3 | 6 | Major | Master/Doctor | Computer Science and Engineering | English | Yes | |
| Machine Learning (ML) techniques have been rapidly adopted in various vital applications. However, ML-based systems are encountering several vulnerabilities, which are threatening the overall security of the system. Several research works have been conducted to identify and shed light on these learning models' fundamental security/privacy problems. Also, various proposals have been made to countermeasures or mitigate these vulnerabilities. Since many graduate students are machine learning practitioners, it is essential to not only develop new learning models, but also be aware of the potential adversarial attacks and the ways to protect the ML model against them. This course help students to learn about the recent cutting-edge attacks and defenses techniques from the adversarial ML domain. Also, the course covers several related topics such as fundamental security concepts, writing secure coding, software security best practices, and software validation approaches and principles. This course requires a good knowledge about machine learning including deep learning and python development to learn the course contents. | |||||||||
| ESW5035 | Advanced Computer Network ArchitectureDesign | 3 | 6 | Major | Master/Doctor | Computer Science and Engineering | - | No | |
| his course is an advanced course of undergraduate network courses, and the prerequisites are undergraduate network courses and operating systems. The main topics are as follows: Software Defined Network (SDN): Open Flow, Network Orchestration Network Function Virtualization: Container, NFV Applications Datacenter Network: RDMA, Delay-based congestion control Mobile/Wireless Network: 4G/5G Network, Wi-Fi, IoT networks | |||||||||
| ESW5037 | Usable Security | 3 | 6 | Major | Master/Doctor | Computer Science and Engineering | - | No | |
| This course focuses on how to design and build secure systems with a human-centric focus. We will look at basic principles of human-computer interaction, and apply these insights to the design of secure systems with the goal of developing security measures that respect human performance and their goals within a system. | |||||||||
| ESW5041 | Digital Health Security | 3 | 6 | Major | Master/Doctor | 1-3 | Computer Science and Engineering | - | No |
| Digital THerapeutics sis one of the rapidly growing field which convergies ICT and Medical technologies. In this seminar, The cutting edge technologies such as IoT, AI, Display, will be presented and discussed. | |||||||||
| ESW5048 | Trustworthy Machine Learning | 3 | 6 | Major | Master/Doctor | 1-4 | Computer Science and Engineering | English | Yes |
| As machine learning (ML) and deep learning (DL) systems are increasingly implemented in real-world applications to improve our lives, it is essential to guarantee that these systems exhibit appropriate and trustworthy behavior. Researchers and practitioners are increasingly interested in developing and deploying ML models and algorithms that are not just accurate, but also explainable, fair, privacy-preserving, causal, and robust. The course helps students learn about current efforts to develop trustworthy machine learning models. The course covers a variety of developing research issues relating to model fairness and transparency, ML/DL explainability, and security and privacy of ML models. The majority of the course contnent and readings will include of both seminal and recent publications in top venues. This course demands a solid understanding of machine learning, particularly deep learning and Python programming, in order to comprehend the course material. | |||||||||
| SIC5011 | Collaborative Research Workshop | 3 | 6 | Major | Master/Doctor | Convergence for Social Innovation | Korean | Yes | |
| Individual research project on closing multiple gaps including economic gap, health/life cycle gap, and AI information technology gap. The topic will be determined under the guidance of an advisor. | |||||||||
| SIC5012 | Social Innovation Convergence Internship | 3 | 6 | Major | Master/Doctor | Convergence for Social Innovation | Korean | Yes | |
| This course is intended to teach students how to translate knowledge learned from the first two semesters to social innovation practices. Students will learn methods for applying theoretical knowledge to real-world industrial settings and making decisions using information. | |||||||||