For more details on the courses, please refer to the Course Catalog
| Code | Course Title | Credit | Learning Time | Division | Degree | Grade | Note | Language | Availability |
|---|---|---|---|---|---|---|---|---|---|
| AIM4003 | Natural Language Processing Fundamentals | 3 | 6 | Major | Bachelor/Master | 1-4 | Artificial Intelligence | Korean | Yes |
| his course covers the overall content of theories and techniques for analyzing and generating natural languages. This course deals with NLP overview, text corpus lexical resources, preprocessing, POS tagging, text vectorization, document classification, syntax analysis, semantic analysis, word embeddings, summarization, deep learning based language models. After taking this course, students are expected to implement programs to solve text problems. To take this course, students are required to have sufficient knowledge in machine learning, deep learning, and Python programming. | |||||||||
| AIM4004 | Intro to AI Agent | 3 | 6 | Major | Bachelor/Master | 1-8 | Artificial Intelligence | - | No |
| This course aims to understand the technical foundations upon which modern AI services, such as ChatGPT, are built and operate. Beyond the working principles of simple models, it provides a broad overview of the full technical stack required to implement commercial-grade AI services. To this end, the course begins with the latest LLM development paradigms, including Transformer-based model structures, pre-training, fine-tuning, and instruction tuning. It then progressively covers key elements from the perspective of implementing actual agent systems, such as prompt engineering, Retrieval-Augmented Generation (RAG), agent planning & reasoning, multi-agent collaboration, and tool use. Furthermore, by analyzing recent research papers and results, the course aims to help students grasp rapidly changing technology trends and cultivate the ability to design and build sophisticated AI systems based on this knowledge. | |||||||||
| 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. | |||||||||
| ESW4004 | Principles of Distributed Computing | 3 | 6 | Major | Bachelor/Master | Computer Science and Engineering | English | Yes | |
| A distributed system is a collection of independent networked computers that function as a single coherent system. With the advent of the fast interconnect and large datasets, a.k.a. Big Data, distributed systems are becoming more important and they are widely used in various domains including AI. The primary goal of this class is to learn key design principles of distributed systems and understand how distributed systems manage resources in a networked environment. Course topics include, but not limited to, communication protocols, processes/threads, naming, synchronization, consistency, and fault tolerance. | |||||||||
| ESW4006 | Information Visualization | 3 | 6 | Major | Bachelor/Master | Computer Science and Engineering | Korean | Yes | |
| With the advances in data storing and processing technologies, the size of data humans confront is increasing at an unprecedented rate. Despite the ever-increasing data size, our perceptual and cognitive abilities stay relatively unchanged, leading to an information gap between humans and data. Information visualization provides one means of addressing such information overload, as well-designed visual representations can assist our perceptual and cognitive abilities to understand, analyze, and memorize the data. In this course, students will learn to 1) design, evaluate, and critique visualization designs, 2) comprehend the characteristics of humans' perception that underpin visualization, 3) understand novel visualization and interaction techniques, and 4) implement interactive data visualizations. The topics of this course will include but not limited to: - Foundations of Information Visualization, Exploratory Data Analysis (EDA), Visual Analytics - Data and Task Abstraction - Mark, Channel, Color, Perception, Interaction, and Animation - Tables, Maps, Networks, Text, and Uncertainty - Visualization for Large-scale and High-dimensional Data - Visualization for the Explainability and Trustworthiness of Machine Learning Methods | |||||||||
| 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. | |||||||||
| ESW4028 | Advanced Topics in Generative Artificial Intelligence | 3 | 6 | Major | Bachelor/Master | Computer Science and Engineering | English | Yes | |
| This course provides an in-depth exploration of the theoretical foundations of Generative AI, and the implementation of cutting-edge agent systems. Students will learn the core principles of Generative AI, including probabilistic generative models, VAEs, GANs, and Diffusion Models, and analyze the latest development paradigms of Large Language Models (LLMs), such as Reasoning Models, Agentic Models, and Simulation-based Reinforcement Learning. The course examines how these theoretical models are implemented into commercial services like ChatGPT from a Full Tech Stack perspective. Through hands-on practice with advanced system-building techniques—including Prompt Engineering, Retrieval-Augmented Generation (RAG), Tool Use, and Multi-agent Collaboration—it aims to equip graduate students wishing to research or apply Generative AI in industry with essential theoretical foundations and practical capabilities. | |||||||||
| ESW4029 | Machine learning and AI for finance | 3 | 6 | Major | Bachelor/Master | Computer Science and Engineering | - | No | |
| The amount of data used in finance field is exponentially increasing, and the trend toward using data goes beyond traditional financial data. Therefore, it is very important to develop the ability to apply Machine Learning methods or AI models to deal with financial tasks in order for students to be competitive and to become leaders in the future of finance. To this end, this course aims to develop the ability to deal with financial data in practice by learning the basics of machine learning, data science and AI, and applying them. | |||||||||
| ESW7002 | Data Modeling for Intelligent Networks and Security | 3 | 6 | Major | Bachelor/Master/Doctor | 1-4 | Computer Science and Engineering | - | No |
| This course aims at the teaching of Data Modeling and Management Automation for Intelligent Networks and Security. For various services in Software-Defined Networking (SDN) and Network Functions Virtualization (NFV), it explains YANG Data Modeling Language that has been developed by IETF (Internet Engineering Task Force) that is the Internet Standardization Organization. Remote control for management automation uses NETCONF and RESTCONF protocols. This course deals with YANG data models for the management automation of intelligent networks and security. Policies and rules on the networks and security can be constructed by the YANG data models that generate XML documents. These XML documents can be delivered to a server (i.e., network or security system) by a client (or administrator) to configure the policies and rules on the target server via NETCONF or RESTCONF. As an example for network security management automation, this course uses the framework and interfaces of Interface to Network Security Functions (I2NSF) in IETF. Through Intent-Based Networking (IBN), this course explains a security policy translator to automatically translate a high-level security policy into the low-level security policy for the requested network security services. | |||||||||
| ESW7004 | Special Topics on Confidential Computing for AI and Data | 3 | 6 | Major | Bachelor/Master/Doctor | Computer Science and Engineering | English | Yes | |
| Special Topics on Confidential Computing for AI and Data for AI is a graduate-level discussion-based course focusing on protecting AI computations and data using state-of-the-art Trusted Execution Environment (TEE) technologies such as Intel SGX, AMD SEV, and Intel TDX. The course covers both defensive techniques and potential attack vectors in TEE-based AI computation. Students will engage in in-depth discussions, analyze research papers, and complete a hands-on project to apply their knowledge in real-world scenarios. | |||||||||
| ESW7005 | Introduction to Parallel Computing | 3 | 6 | Major | Bachelor/Master/Doctor | Computer Science and Engineering | - | No | |
| This course introduces essential parallel processing techniques required in modern computing environments that demand large-scale data processing and high-performance computation. This course covers the architectural characteristics of various parallel systems—including multicore processors, GPUs, and clusters—and explores methods for maximizing program performance using these architectures. Students will learn key concepts such as thread-level parallelism, data parallelism, and task parallelism, and will gain hands-on experience with major parallel programming models and tools such as OpenMP, MPI, and CUDA. The course also focuses on parallel algorithm design, performance analysis, load balancing, and synchronization challenges. By the end of the course, students will have developed practical skills applicable to high-performance computing (HPC), artificial intelligence, scientific computing, and large-scale system development. | |||||||||
| ESW7006 | Advanced Topics in Quantum Computing | 3 | 6 | Major | Bachelor/Master/Doctor | Computer Science and Engineering | - | No | |
| This graduate-level course covers the theory and applications of Quantum Computing, a new computational paradigm based on the principles of quantum mechanics. Students learn fundamental concepts of quantum information, including qubits, superposition, and entanglement, and perform in-depth analysis of quantum circuit models, quantum gate design, measurement, and noise models. The course also explores representative quantum algorithms such as Shor's algorithm and Grover's algorithm, as well as emerging topics in the NISQ era, including variational quantum algorithms (VQA), quantum machine learning, and quantum simulation. | |||||||||
| ESW7007 | Systems for Machine Learning | 3 | 6 | Major | Bachelor/Master/Doctor | Computer Science and Engineering | Korean | Yes | |
| This course focuses on the design and optimization of systems that efficiently support the training and inference of large-scale machine learning models. It covers key topics such as distributed learning architectures, parallel processing strategies, data pipelines, model serving, and scaling. Students learn techniques to enhance machine learning performance from a system perspective, including GPU/TPU accelerator architectures, memory management, and communication and synchronization methods. The course also involves analyzing recent research papers to understand the design principles of large-scale AI systems, and hands-on exercises provide practical experience in setting up distributed learning environments and implementing ML systems. The course aims to equip students with the skills to integrate machine learning and systems knowledge to build high-performance, large-scale ML infrastructure. | |||||||||
| ESW7008 | Hyper-Connected Network Systems | 3 | 6 | Major | Bachelor/Master/Doctor | Computer Science and Engineering | - | No | |
| This graduate-level course covers next-generation network technologies and system architectures that form the foundation of the hyper-connected era. It focuses on network design principles and operational techniques required in hyper-connected environments, including 5G/6G technologies for ultra-high-speed and ultra-low-latency communications, large-scale IoT infrastructures, edge–cloud integrated architectures, and distributed service architectures. The course also analyzes recent research trends in network virtualization (NFV), software-defined networking (SDN), intelligent network management, and security and reliability enhancement techniques. Through hands-on exercises and paper reviews, students develop the skills to design systems that consider the performance, scalability, and reliability of hyper-connected network systems. | |||||||||