Curriculum
| Course Number | Summary | English |
|---|---|---|
| AI7001 | AI and Ethics | |
| This course provides the ethical responsibility in the use of artificial intelligence technology and research. | ||
| AI7002 | Advanced Probability and Random Variables | |
| This course provides various distributions including Gaussian and Poisson, conditional probability, Bayesian theory, algebraic laws, central limit theorem, and so on. | ||
| AI7004 | Advanced Machine Learning | |
| This course provides SVM, kernels, neural networks in supervised learning as well as clustering and dimensionality reduction in unsupervised learning. | ||
| AI7005 | Advanced Deep Learning | |
| This course provides the initializer and the optimizer for deep learning models and how to construct a deep learning model. | ||
| AI7007 | AI Practical Research Project 1 | |
| In this course, the students conduct a research project at the level of incubating AI researcher (Part 1). | ||
| AI7011 | Statistical Learning Theory | |
| In this course, the students learn statistical learning theory including loss and risk. | ||
| AI7014 | Natural Language Processing | |
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This course aims to provide various topics on natural language processing such as document recognition and translation. It covers the techniques of Word2vec, Glove, LSTM, and so on. |
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| AI7015 | Advanced Computer Vision | |
| This course covers from basic image processing to cutting-edge technology in image and video processing domains | ||
| AI7019 | Time Series Data Analysis | |
| In this class, the students learn the overview, implementation, and application examples of Recurrent Neural Network (RNN) which is excellent for natural language processing and time series data analysis. They also learn the structure of LSTM with an additional long-term memory concept and that of GRU, a simplified LSTM. | ||
| AI7022 | Data Mining | |
| This course explains the background, the characteristics, and the success factors of data mining. It introduces the representative techniques of data mining such as classification, cluster analysis, shopping cart analysis, and recommendation. | ||
| AI7023 | Advanced AI Networking | |
| This course introduces the algorithms and design techniques to increase networking performance based on machine learning and optimization techniques. It also explains how to create domain-specific novel learning models in a distributed learning environment. | ||
| AI7024 | Information Retrieval | |
| This course deals with search techniques by statistical, linguistic and semantic methods. It also introduces evaluation methods for search efficiency and various factors that determine the performance of information retrieval systems. | ||
| AI7025 | Reinforcement Learning | |
| This course introduces the reinforcement learning and concept of policy networks through Monte Carlo Tree Search. In addition, it teaches the operation principle according to State, Action, and Reward. | ||
| AI7026 | Continual Learning | |
| Continual Learning is a concept to train a model for a large number of tasks sequentially without forgetting knowledge obtained from the preceding tasks. Through this course, the students learn and design the algorithms for new concepts about continual learning. | ||
| AI7027 | Explainable AI | |
| Explainable AI refers to methods and techniques in the application of artificial intelligence such that the results of the solution can be understood by humans. This class teaches feature interpretation as well as rule induction. | ||
| AI7028 | Intelligent Security | |
| Theories on confidentiality and consistency technology for information that may be exposed in machine learning environments, information protection technology in distributed learning, intelligent detection technology, and privacy protection technology are studied. The students learn practical and development skills through a practical project. | ||
| AI7029 | Artificial Neural Network Processor | |
| In this course, the students understand the classic CPU technology, and the structure and design of ANN processor. | ||
| AI7040 | Generative Model | |
| In this course, the students learn generation algorithms for creating images, texts, and voices. | ||
| AI7042 | AI Reverse Engineering | |
| - | ||
| AI7049 | ontinuum Robotics | |
| In this course, students learn the mechanism and designing method of free-movement continuum robot through ANN-based learning or reinforcement learning. | ||
| BME725 | Future medical technologies | |
| This class helps graduate students to develop an understanding of the limitations of current medical technology and the process of creating and transferring new medical technology from research into actual use(commercialization). Topics include robotic surgery, drug delivery system, and advanced medical devices. | ||
| BME782 | Block chain Technology in Healthcare | |
| This course aims to cover distributed ledger technology, immutable records that cannot be changed or manipulated, and smart contract, which are the core elements of blockchain. It also covers the types of blockchains and networks. This course aims to broaden the understanding of the various use cases of blockchain technology and its application in the healthcare field. In particular, this course covers the establishment of a medical information distribution system using blockchain technology, the establishment of an integrated patient medical information system through blockchain, and the provision of customized medical information according to the characteristics of patients by utilizing the collected patients' big data and blockchain-based smart contract technology. | ||
| CSE7001 | Creative Software | |
| We deal with new technology and standard associated with computer software and prggramming. | ||
| CSE7102 | Optimization Theory | |
| Convex optimization problems arise frequently in many different fields. The focus of this class is on recognizing convex optimization problems and then finding the most appropriate technique for solving them. | ||
| CSE7103 | Graph Theory | |
| We lecture fundamental graph theory in the definition of, type of graph, feature of graph point of view. Also we lecture the path algorithms of graph, coloring of graph, planarity, connectivity etc. | ||
| CSE7201 | Advanced Computer Architecture | |
| This course studies the design techniques for memory hierarchy, I/O system structure, control units, and processors for general-purpose computers. The class also studies the advanced computer architectures such as RISC and VLIW for high-performance computers. | ||
| CSE7202 | Advanced Topics in Operating System | |
| The course is designed to provide students with latest research and development trends in the area of Computing and especially in the area of Context-aware Computing. This is important to announce, so students must know this before registering the course. | ||
| CSE7203 | Advanced Database | |
| This course is prepared for graduate Database course. Here, we will look through advanced usage of database technology. We will provide basic concepts and terminology for Data Warehouse, Decision Support, and Data MIning. | ||
| CSE7204 | Cloud Computing | |
| Understand the concept of cloud computing and proceed with the project. Understand the features of virtual machine and develop projects such as IaaS, PaaS, SaaS. Learn how to use cloud service of Amazon and Google and cloud service development practice using PaaS (Azure, Openshift, Cloudfoundry) and cultivate practical skill of provisioning and operating virtual resources through OpenStack. | ||
| CSE7207 | Query Processing | |
| This class introduces acvanced file architecture to save efficiency the enormous data. It also explains various access plans to extract the required data. We will also study query optimization techniques to select the optimum. | ||
| CSE7209 | Advanced Software Engineering | |
| This course introduces the basic concepts and theories of software engineering. Student will grow their expertise of software development process with practical development project. | ||
| CSE7302 | Advanced HCI | |
| The advanced topics in HCI class first reviews the principles of human-centered design of an interactive digital system. It includes the three core principles-usability, usefulness, and affection-as well as the techniques for analyzing user, task, and technology. Then, the class moves to advanced topics of haptics and virtual reality. This covers computer, machine, and human haptics as well as input and output devices in VR. | ||
| CSE7303 | Advanced Computer Vision | |
| Computer vision is an interdisciplinary research field that deals with how computers can be made for gaining high-level understanding from digital images or videos. In this class we study following topics: Feature Extraction, Visual Tracking, Shape Matching, Pattern Detection, Object Recognition, Gesture Recognition, Scene Understanding, 3D Reconstruction. | ||
| CSE7305 | Moving Picture Coding | |
| We will study the video coding algorithm, especially MPEG algorithm. | ||
| CSE7402 | Advanced Computer Networking | |
| This course introduces next generation service support technology, stream technology in core/distribution/access network to support high quality/high efficiency and system architecture requirement and considerations. | ||
| CSE7403 | Mobile Communication Networks | |
| This lecture provides basic theory of mobile communication networks such as GSM, CDMA, WCDMA, HSPA, and LTE including MAC layer, signaling plane, bearer plane, access network, and core network, in view of protocols and its operation. In addition, by doing project, students will understand how the MAC and service layer protocol is implemented through network simulators and open source SIP engines. Therefore, students have detail knowledge using current mobile communication network technology. | ||
| CSE7404 | Future Internet | |
| In this course, we review the current Internet and investigate its problems. We will also examine the evolutionary and revolutionary approaches for designing the Future Internet. And we will focus on the manageability aspect of the Future Internet. We will then discuss and come up with possible areas or methods for overcoming the existing problems for the Future Internet. So, this class will be discussed principles and technologies arising from the need to spread the research areas of Future Internet. | ||
| CSE7405 | Open Source Networking | |
| Students achieve practical software development and verification capability for the paradigm shift to open source software and hardware networking through SDN/NFV(Software Defined Networking/Network Function Virtualization). | ||
| CSE7406 | Advaced Information Security | |
| This course covers different aspects of security and privacy of data residing on some storage medium or communicated over a communication line. The aim is to help students understand the important concepts of security. Security is concerned with the protection of data and ensures privacy through various security mechanisms. | ||
| CSE7501 | Advanced Algorithms | |
| Students will learn advanced algorithm techniques and classification for specific area. | ||
| CSE7503 | Machine Learning | |
| This course demonstrates the practical use of machine learning algorithms. To transmit the theoretical basis of machine learning, understand its basic building blocks and general principles that allow one to design machine learning algorithms To become familiar with specific, widely used machine learning algorithms including neural networks, decision trees, k nearest neighbors, association rules mining and various unsupervised learning systems To learn methodology and tools in lab practice to apply machine learning algorithms to real data and evaluate their performance To transmit the theoretical basis of machine learning, understand its basic building blocks and general principles that allow one to design machine learning algorithms To become familiar with specific, widely used machine learning algorithms including neural networks, decision trees, k nearest neighbors, association rules mining and various unsupervised learning systems To learn methodology and tools in lab practice to apply machine learning algorithms to real data and evaluate their performance | ||
| CSE7508 | Knowledge Representation and Reasoning | |
| This course learn how to express knowledge by computer such as logic, frame, etc. to enhance the utility of the Deductive inference through ontology and semantic Web. | ||
| CSE7509 | Advanced AI Networking | |
| This course analyze the operation pattern and understand calculation pattern of artificial neural network computation processing, and acquires HW building block design, NPU architecture design, NPU programming interface, NPU compiler learning and implementation technology. | ||
| CSE7511 | Advanced IoT | |
| This course introduces sensor network protocols that are the basis of IoT, especially energy-saving routing, security technology, and sink mobility technology. | ||
| CSE7513 | Advanced Linear Algebra | |
| This course studies linear programming and integer programming after learning the basic knowledge about eigenvalues, eigenvectors, orthogonality, symmetry, linear transformation and row decomposition. | ||
| CSE7514 | Cognitive Psychology | |
| This course acquire basic knowledge of humanitas convergence education and cognitive psychological knowledge which is the basis of AI and SW. | ||
| CSE7518 | Advanced VR/AR | |
| This course learn a way to make a new idea and deep-analysis about latest display technology and interaction technology in virtual reality/ augmented reality domain. | ||
| CSE7519 |
Computational imaging |
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| This course acquire the latest technology related to 3D imaging through acquisition/processing of SW-based three-dimensional data, such as point image, depth sensor, coded sensor and volumetric camera. | ||
| CSE7520 | Advanced Intelligent Media | |
| This course introduce basic concept and theory for intelligent visual media processing, which combines artificial intelligence technology and digital media technology. It also covers the related research trends and standards of intelligent media processing. The prerequisite for this course is knowledge on machine learning and deep learning. | ||
| CSE7521 | Advanced Probability and Statistics | |
| - | ||
| CSE7522 | Machine Learning | |
| Machine Learning is concerned with computer programs that automatically improve their performance through experience. This course covers the theory and practical algorithms for machine learning from a variety of perspectives. We cover topics such as Bayesian networks, decision tree learning, Support Vector Machines, statistical learning methods, unsupervised learning and reinforcement learning. The course covers theoretical concepts such as inductive bias, the PAC learning framework, Bayesian learning methods, margin-based learning, and Occam's Razor. | ||
| CSE7523 | Advanced Numerical Analysis | |
| In this class, we study methods using numerical approximations for the problems of mathematical analysis. Topics cover interpolation, extrapolation, regression, solving eigenvalue or singular value problem and optimization. | ||
| CSE7524 | Deep Learning based Image Processing | |
| This course introduces recent research and standardization efforts on Deep-Learning based Visual Data Processing. Common basis such as related loss functions, rate-performance optimization, usecases and related standardization will be covered first. Then, some of the real application research such as learned super-resolution, learned image/feature compression will be covered. As a term-project, students are need to submit and present their own work related to this area. | ||
| CSE8001 | Special Lecture on Creative Software 1 | |
| We deal with new technology and standard associated with computer software | ||
| CSE8002 | Special Lecture on Creative Software 2 | |
| We deal with new technology and standard associated with computer software | ||
| CSE8101 | Advanced Numerical Analysis | |
| In this class, we study methods using numerical approximations for the problems of mathematical analysis. Topics cover Numerical Methods, Gauss Elimination, Eigenvalue Linear Regresson, Fourier Analysis. | ||
| CSE8301 | Advanced Computer Graphics | |
| This course introduces 3D modeling for visualizing, generating of computer graphic and conversing of 2D and 3D, theory about shading and the latest study trend. And this lectures provide the chance to make the 3D modeling in practice and simulation system using OpenGL. | ||
| CSE8302 | High Efficiency Video Coding | |
| We will study the advanced video coding algorithm, especially top of video coding algorithm. | ||
| CSE8303 | Digital Holography | |
| This is an advanced class for graduate students who have background knowledge in image processing and computer vision. Basic principles and state of the art methods in 3D imaging, computational imaging and processing such as multi-view stereo, RGBD based 3d reconstruction, lens-array (plenoptic camera), digital holography, coded-X imaging including corresponding 3d display technologies. Classes are composed of several lectures on the technologies, survey on cutting edge papers, student presentations and discussion. |
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| CSE8504 | Research Ethics & Technical Writing | |
| This course introduce how to write down the research paper and what is research ethics. | ||
| EE7117 | Reinforcement Learning | |
| This lecture earns the concept, purpose, and components of reinforcement learning based on the Markov Decision Process (MDP). The prediction and control are studied to learn the optimal policy in Markov Decision Process(MDP) using Bellman equation. In order to train the optimal policy from the actual episodes, starting from the Monte Carlo method., Q-learning, SARSA, and Time Difference (TD) are studied. Algorithms such as DQN, AC, and A3C are learned to apply reinforcement learning to actual tasks which are non-MDP situations. | ||
| EE716 | Sensor-based Mobile Robots | |
| This course covers all aspects of mobile robot systems design and programming from both a theoretical and a practical perspective. The basic subsystems of control, localization, mapping, perception, and planning are presented. For each, the discussion will include relevant methods from applied mathematics. aspects of physics necessary in the construction of models of system and environmental behavior, and core algorithms which have proven to be valuable in a wide range of circumstances. | ||
| EE792 | Artificial Intelligence Integrated Circuits | |
| In this course, computing architectures for deep learning will be analyzed. In particular, the mechanizm behind deep learning will be analyzed in terms of circuit efficiency and various techniques employed to achieve the efficiency will be studied. The focus of this course will not only be on optimized hardware for inference, but also on learning. | ||
| EE793 | Wearable convergence semiconductor | |
| In this course, variety of renewable energy related convergence semiconductor implementations will be analyzed and their applications will be studied. In particular, suitable structures and methods to apply the renewable energy for wearable applications will be explored in detail. | ||
| EIC7016 | Machine Learning and Pattern Recognition | |
| This course contains a series of PBL type lectures on machine learning and its applications on pattern recognition. | ||
| EIC7036 | Convergence Future Communication Colloquium I | |
| This colloquium contains a series of seminars discussing the current theoretical developments and industrial trends on convergence future communication technologies. | ||
| EIC7040 | Wireless Networks | |
| This course contains a series of PBL type lectures on wireless and mobile networks and their recent developments. | ||
| EIC7045 | Distributed Networks | |
| This course contains a series of PBL type lectures on modern techniques for distributed networks such as edge computing, wireless caching, and distributed learning, toward the distributed system integrating them. | ||
| EIC7047 | Deep-learning programming | |
| This course contains a series of PLB type lectures on deep learning fundamentals and programming methods for deep learning. | ||
| SWCON7015 | Seminar on Game Analysis | |
| We will deal with the history of major games from 1970s, when the first commercially available video game was introduced. We will learn how games with purposes other than entertainment have advanced. We will categorize games after 2010 and discuss what roles will games play in modern society. | ||
| SWCON7016 | Seminar on Game Industry | |
| We will deal with past and present of game industry. We will discuss its facing problems and propose direction of the game industry. People working in game industry will be invited to give talks and discuss the relevant issues. | ||
| SWCON7018 | Brain AI | |
| The human brain is made up of neural networks, and brain-inspired AI technology refers to the process of creating artificial neural networks that work the way the human brain works. Study the neuroscience theory for the development of artificial intelligence algorithms that resemble the working principle of the brain and learn about the brain-inspired AI technology methodology. In this course, students learn about artificial intelligence models and neuroscience theories for learning, linear models, shallow neural networks, and deep learning core models. | ||
| SWCON7021 | Robot Vision and Sensing | |
| One of the most important abilities of a mobile robot is spatial sensing. In particular, vision sensing enables robot to navigate, avoid obstacles, recognize objects by using high performance cameras. New 2D and 3D vision sensing technologies improves the robot’s safety, confidence of its motion, and eventually its productivity. In this course, we will handle various sensors such as cameras, laser scanners, IMU and GPS for spatial sensing of a robot and learn how to integrate the different sensor data in computer vision algorithms. | ||
| SWCON7023 | Technology for Extended Reality, Advanced | |
| In this course, we learn the trend technologies and related theories regarding extended reality. Representative technologies include redirected walking, environment recognition, human perception in the virtual reality. This course focuses on basic advanced theories. | ||
| SWCON7027 | Artificial Intelligence for Healthcare | |
| Healthcare is one of the most exciting application domains of artificial intelligence, with transformative potential in areas ranging from medical image analysis to electronic health records-based prediction and precision medicine. This course will involve a deep dive into recent advances in AI in healthcare, focusing in particular on deep learning approaches for healthcare problems. We will start from foundations of neural networks, and then study cutting-edge deep learning models in the context of a variety of healthcare data including image, text, multimodal and time-series data. In the latter part of the course, we will cover advanced topics on open challenges of integrating AI in a societal application such as healthcare, including interpretability, robustness, privacy and fairness. The course aims to provide students from diverse backgrounds with both conceptual understanding and practical grounding of cutting-edge research on AI in healthcare. | ||
| SWCON7029 | Image Recognition Using Machine Learning | |
| In this course, students learn how to make object classification, object detection, object tracking, and pose estimation using machine learning. By understanding the architectures, learning process, and analysis for models using various machine learning including deep learning, we study how to design higher performance models than the existing methods. | ||
| CSE7525 | Advanced Topics in Security Convergence | |
| This course covers the core theories and practical techniques of information security, and explores advanced security issues and solutions in the industry. It investigates new challenges that arise as traditional information security problems are applied to various new industrial sectors (such as IoT, unmanned vehicles, AI, etc.) and studies measures to address them. | ||
| CSE7526 | Introduction to UAV | |
| This course will cover and learn the basic principles, technologies, applications, and research trends of unmanned vehicles. Topics include the types of unmanned vehicles, various security issues, the operating mechanisms of unmanned vehicle systems, communication protocols, and we will explore related academic papers. | ||
| CSE7527 | Security Convergence Project A | |
| This is a PBL (Project Based Learning) course for Ph.D. and masters candidates. | ||
| CSE7528 | Security Convergence Project B) | |
| This is a PBL (Project Based Learning) course for Ph.D. and masters candidates. | ||
| CSE7529 | Security Convergence Project C | |
| In this course, students learn how to make object classification, object detection, object tracking, and pose estimation using machine learning. By understanding the architectures, learning process, and analysis for models using various machine learning including deep learning, we study how to design higher performance models than the existing methods. | ||
| CSE7530 | Security Convergence Project D | |
| This is a PBL (Project Based Learning) course for Ph.D. and masters candidates. | ||
| CSE7531 | Web Hacking | |
| The course will cover the basic theories of web hacking, attack techniques, and countermeasures for web security, and delve deeper into the integration of cloud systems in unmanned vehicle systems. From security issues in web development frameworks, such as basic SQL Injection, to logical bugs like SSTI and SSRF that have become increasingly important as cloud systems are utilized and web systems become more complex, we will explore related research papers | ||
| CSE7532 | Compiler for Security | |
| In this course, a variety of compiler-based security techniques are studied. The topics of the course include software testing through static and dynamic program analysis and automatic program transformation for security and privacy. In addition, the security-related features provided by existing compilers such as gcc and clang are investigated. | ||
| CSE7533 | Introduction to Cryptography | |
| - | ||
| CSE7534 | ntroduction to UAV dynamics | |
| This course study the kinematics, dynamics, and control theory of unmanned vehicles, explore optimization techniques, and investigate related theories from a mechanical engineering perspective. | ||
| CSE7535 | ntroduction to UAV Communication and Sensors | |
| To understand the mobile communication system, this lecture covers the characteristics of wireless channel, the concept of cellular system and the architecture of mobile communication system. It includes the key technologies of next generation mobile communication system. | ||
| CSE7536 | Introduction to UAV-Big-data and AI | |
| The course study and explore data processing, analysis, and artificial intelligence-based optimization methods for unmanned vehicles. We investigate methods for effectively handling big data using AI technology, as well as the theories and academic research trends in areas where such systems can be applied. | ||
| CSE7537 | Advanced Security Convergence Project 1 | |
| This is a PBL (Project Based Learning) course for M.S. integrated Ph.D. candidates. | ||
| CSE7538 | Advanced Security Convergence Project 2 | |
| This is a PBL (Project Based Learning) course for M.S. integrated Ph.D. candidates. | ||
| EE716 | Sensor-based Mobile Robots | |
| This course covers all aspects of mobile robot systems design and programming from both a theoretical and a practical perspective. The basic subsystems of control, localization, mapping, perception, and planning are presented. For each, the discussion will include relevant methods from applied mathematics. aspects of physics necessary in the construction of models of system and environmental behavior, and core algorithms which have proven to be valuable in a wide range of circumstances. | ||
| EE741 | Advanced Mobile Communication | |
| To understand the mobile communication system, this lecture covers the characteristics of wireless channel, the concept of cellular system and the architecture of mobile communication system. It includes the key technologies of next generation mobile communication system. | ||
| EE742 | Wireless Communication Systems I | |
| This course provides selected topics in wireless communications for adaptation to the advent of new theories and technologies. The topics may be selected from recent research areas such as multiple-input multiple-output communications, multiuser communications, orthogonal frequency division multiplexing, advanced coding theory, etc. | ||
| ME725 | Compressible Fluid Dynamics | |
| This lecture introduces the flow phenomenon including shock and expansion wave related to compressible fluid flow, and discusses its analysis method. | ||
| SWCON7028 | Advanced Applications of Signal Processing | |
| This course introduces characteristic, processing, and analysis methods for both one-dimensional and two-dimensional signals such as audio, active sensor data, image, and so forth. By understanding various filters, synthesis, and recognition methods that utilize temporal, spatial, and frequency data, students learn how to use them in various application systems such as autonomous vehicles and intelligent robots. |