For more details on the courses, please refer to the Course Catalog
| Code | Course Title | Credit | Learning Time | Division | Degree | Grade | Note | Language | Availability |
|---|---|---|---|---|---|---|---|---|---|
| ESW5023 | Text Mining and Analytics | 3 | 6 | Major | Master/Doctor | - | No | ||
| Text mining and analytics is the process of discovering hidden knowledge from text data. Basically, text mining involves text categorization, text clustering, concept/entity extraction, sentiment analysis, document summarization, and entity relation modeling. Text analysis involves information retrieval, lexical analysis for word frequency distributions, and information extraction. The essential goal is to turn text into data for analysis via natural language processing (NLP) and analytical methods. In this course, we will cover the major techniques for text mining and analytics to discover interesting patterns and to extract useful knowledge, based on statistical approaches. We will also implement text mining techniques using Python library. For prerequisite courses, I strongly recommend that you take basic computer programming, data structures, algorithms, data mining, and machine learning. | |||||||||
| ESW5024 | Advanced Data Analysis | 3 | 6 | Major | Master/Doctor | 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 | - | 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 | - | 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 | - | 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 | - | 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 | 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. | |||||||||
| ESW5030 | Advanced Topics in Computer Systems | 3 | 6 | Major | Master/Doctor | - | No | ||
| Students will learn the latest technology and research trends to improve the performance, stability, scalability, security, and energy efficiency of various types of computer systems from mobile to cloud computing. Students study the advances of computer systems and their underlying principles with the emergence of new application programs, new computing environments, and new hardware. | |||||||||
| ESW5031 | Advanced Topics in Computer Architecture Design | 3 | 6 | Major | Master/Doctor | 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 | - | 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. | |||||||||
| ESW5033 | Wired-and-Wireles Networks and Security | 3 | 6 | Major | Master/Doctor | - | No | ||
| This course introduces technologies and standards of wired-and-wireles networks and security. This course explains the basic architectures and operation principles of physical layer, data link layer, network layer, transport layer, and aplication layer that construct the Internet in OSI (Open Systems Interconection) model. As advanced topics, this course explains Software-Defined Networking (SDN), Network Functions Virtualization (NFV), and Cloud-Based Security Service Systems. It also explains the networking and security in vehicular networks. The contents of this course are as folows. - Wek 1: Introduction to Wired-and-Wireles Networks and Security - Wek 2: Internet and Computer Networks - Wek 3: Data Link Layer (1/2) - Wek 4: Data Link Layer (2/2) - Wek 5: Network Layer (1/2) - Wek 6: Network Layer (2/2) - Wek 7: Wireles and Mobile Networks - Wek 8: Term Project Proposal - Wek 9: Transport Layer - Wek 10: UNIX Socket Programming - Wek 11: Multimedia Networking - Wek 12: Aplication Layer - Wek 13: Software-Defined Networking (SDN), Network Functions Virtualization (NFV), and Cloud-Based Security Service Systems - Wek 14: Vehicular Networking and Security - Wek 15: Term Project Presentation This course wil be delivered in the format of a fliped clas. It has four homeworks and one term project. | |||||||||
| ESW5034 | Machine Learning Security and Robustness | 3 | 6 | Major | Master/Doctor | 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 | - | 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 | |||||||||
| ESW5036 | Advanced Topics In Security For Digital Healthcare Devices | 3 | 6 | Major | Master/Doctor | - | No | ||
| This class covers security topics related to digital devices in healthcare domain, such as embedded systems and sensor devices. We study the potential security threats in the domain and discuss the mechanisms to protect the system from the security threats. Digital devices that processes bio information demands a strong security guarantees since those devices are especially sensitive to security issues including personal information protection. To this end, we study general computer system and communication security issues and technologies and standards for personal information protection. | |||||||||
| ESW5037 | Usable Security | 3 | 6 | Major | Master/Doctor | - | 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. | |||||||||