For more details on the courses, please refer to the Course Catalog
| Code | Course Title | Credit | Learning Time | Division | Degree | Grade | Note | Language | Availability |
|---|---|---|---|---|---|---|---|---|---|
| ESW4014 | Principles of Reinforcement Learning | 3 | 6 | Major | Bachelor/Master | Computer Science and Engineering | Korean | Yes | |
| In this course, students learn the basic theory algorithm of Reinforcement Learning (RL) to find the optimal policy for a given environment. From basic reinforcement learning theories such as Markov Decision Process, Planning, and Q-learning to deep neural network-based reinforcement algorithms such as Value Function Approximations and Policy Gradient Methods. In addition, Model-based RL through estimating environments, Exploitation & Exploration Trade-off, and Inverse RL that mimics the behavior of experts are also covered. Basic knowledge of data structures, algorithms and machine learning is required to take this course. | |||||||||
| ESW4024 | Introduction to Recommender Systems | 3 | 6 | Major | Bachelor/Master | 1-4 | Computer Science and Engineering | - | No |
| Recommendation systems aim to use the user's click/purchase history and the content information of items to predict the user's hidden preferences and to provide items that the user would like to prefer. The recommendation systems have been widely used in various domains, such as Web applications, online streaming services, and E-Commerce. This course covers the basic concepts and implementations of various recommender models. We deal with collaborative filtering (CF), which utilizes only user history, and content-based filtering (CBF), which utilizes the similarity between items. Specifically, CF models include conventional neighbor-based and model-based methods for linear and non-linear models using deep neural networks. We also investigate factorization machines and sequential-based recommender models. Furthermore, we implement various recommender models and evaluate them. | |||||||||
| ESW4025 | Artificial Intelligence Ethics | 3 | 6 | Major | Bachelor/Master | Computer Science and Engineering | English | Yes | |
| With the current development of artificial intelligence, we can meet them in various parts of society. However, artificial intelligence is an amoral that cannot make ethical judgments on its own. Therefore, we need to understand and solve various ethical problems caused by artificial intelligence. In this class, we will look at the ethical problems, causes, and solutions of artificial intelligence. First, we will briefly learn about artificial intelligence and then look at the ethical problems they have. They can be largely divided into data, algorithms, and applications, so we will look at each of them. We will then look at the causes of these problems. In addition, we will look at algorithms and examples that actually solve them based on the analyzed problems and causes. | |||||||||
| ESW4026 | Computer Networks and Artificial Intelligence | 3 | 6 | Major | Bachelor/Master | Computer Science and Engineering | - | No | |
| This course aims at the improvement of the capability to apply Artificial Intelligence (AI) technology to the Internet where AI is one of core technologies in the 4th industrial revolution. For this aim, it explains the fundamental protocols and systems of computer networks and security, and the AI technology which can be applied to the computer networking technology. To prepare for 6G network era, it deals with Intent-Based Networking (IBN), Cloud-based Security Services, Intelligent Internet of Things, and Wireless Networking for autonomous vehicles. Especially, AI-based networking and security technologies are handled for 6G core networks. Also, this course teaches students the protocol layers such as data link layer, network layer, transport layer, and application layer. The contents of this course consist of introduction for 1 week, 6G network technologies for 5 weeks, and computer network layers for 8 weeks. As the expected benefits of this course, the students not only learn the foundational knowledge of computer networks and security, but also, for the 4th industrial revolution era, they can be educated up to SW-AI experts who can graft the technologies of the AI and Machine Learning (ML) to the computer networking field. | |||||||||
| ESW5010 | Advanced Operating Systems | 3 | 6 | Major | Master/Doctor | 1-4 | Computer Science and Engineering | Korean | Yes |
| This course introduces the concepts, architectures, and functions of operating systems, and deeply discusses some major functions of operating systems, such as file systems, process management, processor management, memory management, and I/O management at the kernel level. In detail, core mechanisms of each function of the Unix and Linux kernel are introduced and discussed. With this course, the students will get the practical capabilities in designing and improving the operating system functions. | |||||||||
| ESW5010 | Advanced Operating Systems | 3 | 6 | Major | Master/Doctor | 1-4 | Computer Science and Engineering | Korean | Yes |
| This course introduces the concepts, architectures, and functions of operating systems, and deeply discusses some major functions of operating systems, such as file systems, process management, processor management, memory management, and I/O management at the kernel level. In detail, core mechanisms of each function of the Unix and Linux kernel are introduced and discussed. With this course, the students will get the practical capabilities in designing and improving the operating system functions. | |||||||||
| ESW5012 | Topics in Real-Time Systems for Software Platforms | 3 | 6 | Major | Master/Doctor | 1-4 | Computer Science and Engineering | English | Yes |
| This course studies classic real-time systems' theories, and then investigates the-state-of-the-art issues of real-time systems towards supporting software platforms. First, fundamental scheduling theories are covered, including scheduling for the basic real-time task model in uniprocessor/ multiprocessor/cluster platforms, as well as that for the fork-join model and synchronization. Based on the theoretical background, up-to-date papers for real-time systems are studied so as to support software platforms. | |||||||||
| ESW5012 | Topics in Real-Time Systems for Software Platforms | 3 | 6 | Major | Master/Doctor | 1-4 | Computer Science and Engineering | English | Yes |
| This course studies classic real-time systems' theories, and then investigates the-state-of-the-art issues of real-time systems towards supporting software platforms. First, fundamental scheduling theories are covered, including scheduling for the basic real-time task model in uniprocessor/ multiprocessor/cluster platforms, as well as that for the fork-join model and synchronization. Based on the theoretical background, up-to-date papers for real-time systems are studied so as to support software platforms. | |||||||||
| ESW5014 | Advanced Topics in Computer Graphics | 3 | 6 | Major | Master/Doctor | 1-4 | Computer Science and Engineering | - | No |
| This course covers fundamental theories, advanced techniques, and practice in computer graphics. The theories covered in this course include images, geometry, modeling, transformation, projection, shading, texture mapping, ray tracing, global illumination, and special effects. The course also includes practical techniques to implement the theories using graphics processors. | |||||||||
| ESW5014 | Advanced Topics in Computer Graphics | 3 | 6 | Major | Master/Doctor | 1-4 | Computer Science and Engineering | - | No |
| This course covers fundamental theories, advanced techniques, and practice in computer graphics. The theories covered in this course include images, geometry, modeling, transformation, projection, shading, texture mapping, ray tracing, global illumination, and special effects. The course also includes practical techniques to implement the theories using graphics processors. | |||||||||
| ESW5023 | Text Mining and Analytics | 3 | 6 | Major | Master/Doctor | Computer Science and Engineering | - | 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. | |||||||||
| 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. | |||||||||
| 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. | |||||||||
| 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. | |||||||||