Curriculum
| Course Number | Summary | English |
|---|---|---|
| AI7042 | AI Reverse Engineering | |
| . | ||
| AI7001 | AI and Ethics | |
| This course provides the ethical responsibility in the use of artificial intelligence technology and research. | ||
| 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). | ||
| AI7008 | AI Practical Research Project 2 | |
| In this course, the students conduct a research project at the level of incubating AI researcher (Part 2). | ||
| AI7009 | Deep Learning Practice | |
| In this course, the students practice widely-used deep learning models, and conduct intensive training experiments for application tasks. | ||
| AI7011 | Statistical Learning Theory | |
| In this course, the students learn statistical learning theory including loss and risk. | ||
| AI7014 | Natural Language Processing | |
| 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. | ||
| AI7015 | Advanced Computer Vision | |
| This course covers from basic image processing to cutting-edge technology in image and video processing domains. | ||
| AI7016 | Knowledge Representation and Inference | |
| This course aims to introduce how to represent human knowledge through frame and logic to increase the effectiveness of inference. | ||
| AI7017 | Convolutional Neural Network | |
| This course aims to introduce the architecture of CNN, implementation and utilization, image analysis and unstructured data analysis and problem solving techniques with Session-based Deep Learning. | ||
| AI7018 | Optimization Theory | |
| This course introduces optimization problems occurring in various fields and their solutions. In addition, it covers from basic concepts such as convex sets, functions, and optimization problems to their solution and optimization. | ||
| 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. | ||
| AI7020 | Machine Learning and Data | |
| This course introduces the techniques related to data processing such as data processing, handling, cleaning and filtering. | ||
| AI7021 | Graph Theory | |
| This course provides graph theory, Bayesian networks, sampling, and MAP reasoning which are widely used in machine learning, computer vision, and natural language processing. | ||
| 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. | ||
| AI7030 | Smart Healthcare | |
| This course deals with the introduction and trends of ICT technology used in the medical field as well as ICT technology for the development of AI medical treatment and healthcare services from the viewpoint of software engineering. | ||
| AI7031 | Future Car Programming | |
| In this course, the students understand the hardware of a future car and a robot, and develop core softwares directly through learning various elemental technologies such as sensors, LiDAR, Point-Cloud, and computer vision. | ||
| AI7032 | Implementation of Intelligent Medical Service | |
| Utilizing the AI-Medical Platform, the students learn the theory to build an intelligent medical examination, treatment, and post-management application system (AI-Silo) for specific diseases. | ||
| AI7033 | Self-Driving Robot | |
| After having a general overview of self-driving vehicles and moving robots, sensors, and driving device, the students learn autonomous movement technology for two wheeled robots. | ||
| AI7034 | Medical Image Processing | |
| This course deals with the introduction and trends of the image technology used in the medical field such as image processing, recognition, and judgment. | ||
| AI7035 | Memory Element and Neuromorphic Semiconductor System | |
| In this course, the students understand neuromorphic semiconductor systems that are closer to the human brain than conventional von Neumann structures in order to overcome the limitations of existing transistor technologies. | ||
| AI7036 | Intelligent Semiconductor | |
| This course introduces various memory such as SRAM, DRAM, NAND FLASH, MRAM, and so on, as well as principles and production processes for AI processing in-memory techniques, and elemental micro-process techniques. | ||
| AI7040 | Generative Model | |
| In this course, the students learn generation algorithms for creating images, texts, and voices. | ||
| AI7043 | AI-based Healthcare | |
| Students learn the artifical intelligence platform for the construction of an AI-based CDSS, and understand cohor-based healthcare big data collection, processing, and management system. | ||
| AI7044 | VLSI and Computer System | |
| In this course, students learn the latest technologies to understand system semiconductors and computer system mutually requried in industry and techology. | ||
| AI7046 | Digital Health and PHR | |
| In this course, students learn Electronic Health Record(EHR) system and utilization of patient-generated-Health-Data (PGHD). | ||
| AI7047 | Medical Robots and Applications | |
| In this course, students learn the latest technology trends of various medical robots and explore how to expand applications by applying AI technologies. | ||
| AI7048 | Production and Logistics System Optimization | |
| In this course, students learn optimization, simulation, and AI-based methodologies to design and operate the production and logistics systems and apply them to real systems. | ||
| AI7049 | Continuum Robotics | |
| In this course, students learn the mechanism and designing method of free-movement continuum robot through ANN-based learning or reinforcement learning. | ||
| AI7050 | Medical Image and Biosignal Processing | |
| Students learn and practice overview of biomedical image analysis. Students understand the application of the process of time series biosignal processing. | ||
| AI7051 | Medical Aritificial Intelligence | |
| In this course, students learn drug clinical trial fundamentals and statiscal reasoning and data analysis of real world data and clinical unstructured data. | ||
| AI7052 | Artificial Intelligence Quality Management | |
| In this course, students learn the latest quality engineering applications in the various industrial sites. | ||
| AI7054 | Processor-in-memory Neuromorphic Chip | |
| - | ||
| AI7055 | 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. | ||
| AI7056 | Innovative Technology Management and Leadership | |
| This course provides Innovative technology-based corporate management and case-based innovation leadership models for sustainable growth in the era of the 4th Industrial Revolution | ||
| AI7057 | Advanced Smart Energy | |
| The analysis on energy consumption becomes the one of the most important factors to consider in the modern industry and society. Thus, we need to learn, understand, and analyze how energy is created, transformed, and consumed to improve the energy efficiency and reduce its consumption. More specifically, students in this course will learn the basic concepts in energy areas and be trained so that they can perform energy assessments on manufacturing systems. This course will be designed in such a way that students without any previous knowledge on energy can readily learn course materials during this semester. | ||
| AI8037 | AI Creative Research Project 1 | |
| In this course, the students conduct a research project at the level of writing excellent international conference paper (Part 1). | ||
| AI8038 | AI Creative Research Project 2 | |
| In this course, the students conduct a research project at the level of writing excellent international conference paper (Part 2). | ||
| AI8039 | AI Advanced Research Project 1 | |
| In this course, the students conduct a research project at the level of writing top-tier conference paper (Part 1). | ||
| AI8041 | AI Advanced Research Project 2 | |
| In this course, the students conduct a research project at the level of writing top-tier conference paper (Part 2). | ||
| BME725 | Future medical technologies | |
| aguuThis 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.lga | ||
| BME751 | Bio Signal Processing | |
| This class covers basic theories of deterministic and probabilistic methods of signals and systems analysis. Topics include Fourier transform, Laplace transform, z-transform, random variables, random process, probability density functions, correlation functions, spectral analysis, and time-series analysis. | ||
| BME780 | Applications in Deep Learning | |
| This course aims to cover various deep learning models based on the basic principles of deep learning. In this course, students will learn unsupervised learning such as adversarial generative networks (GANs), including existing supervised learning, and implement the code based on the theory. This course aims to broaden the understanding of deep learning applications using data such as various images and signals, and to cultivate experts who can lead a new paradigm of artificial intelligence. | ||
| 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. | ||
| CSE7101 | Advanced Probability and Stastics | |
| This course covers the fundamentals of probability theory including probabilistic models, discrete and continuous random variables, multiple random variables, and limit theorems, which are typically part of a first course on the subject. It also contains a number of more advanced topics, such as, random variable transforms, a more advanced view of conditioning, sums of random variables, and a fairly detailed introduction to Bernoulli, Poisson, and Markov processes. | ||
| 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. | ||
| CSE7510 | Advanced AI Networking | |
| This course deal with the core technology of Internet protocol and the structure recently studied in future Internet groups and learn how to apply machine learning techniques such as CNN or RNN reinforcement learning and big data model-based networking solutions. | ||
| 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. | ||
| CSE7521 | Advanced Probability and Statistics | |
| - | ||
| 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. | ||
| 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. | ||
| 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." | ||
| 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. | ||
| EIC7007 | Artificial Intelligence | |
| This course covers fundamental topics on artificial intelligence, including machine learning and pattern recognition. | ||
| EIC7016 | Machine Learning and Pattern Recognition | |
| This course contains a series of PBL type lectures on machine learning and its applications on pattern recognition. | ||
| EIC7037 | Convergence Future Communication Colloquium II | |
| 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. | ||
| IE733 | Digital Manufacturing | |
| Digital manufacturing is the course to lean manufacturing IT component as well as Computer aided solutions in order to improve the productivity and interoperability by using cyber physical system. | ||
| IE736 | Financial Optimization for Investment Management | |
| The job of planning, implementing, and overseeing the funds of an individual investor or an institution is referred to as investment management. The purpose of this course is to describe the process of investment management and optimization techniques employed for investment management. We will study topics relevant to investment management including but not limited to: traditional portfolio selection, asset pricing, robust portfolio management techniques, and multi-period portfolio optimization models. | ||
| IE740 | Industry-academic cooperation project II | |
| In this course, students perform a industry-academic cooperation project to define a practical field problem and solve it with industry experts. The students can learn their problem-solving abilities by experiencing the field problems that companies face in real industry. | ||
| IE759 | Introduction to Smart Factory | |
| Smart factory means an dramatically enhanced manufacturing environment of integrating advanced ICT such as IIoT, Cloud, CPPS, Big data and AI to manufacturing. This course provides the core technology, trend and case study of smart factory to improve the understandings of smart factory, which is the core concept of the 4th industrial revolution. | ||
| IE766 | Intelligent products & standards | |
| IProduct intelligence is defined as an automated system that collects and analyzes intelligence about the performance of products being designed and manufactured, and this data is automatically fed back to product designers and engineers developing the product to assist in product development. It is a full life cycle product system concept. Learn related information systems, product design methodologies, and operation methods for peripheral devices. | ||
| ME7121 | Introduction to AI-Robot-based Human-Machine Collaboration Technology | |
| The course aims to provide basic knowledge and various concepts used for the human-machine collaboration, particularly collaboration between the human and the AI-based robot. The Ai-based robot is the robot which utilizes the AI technique for the realization of the essential functions of the robot that includes the environment sensing, judgement, and the actuation. This course introduces the various techniques used for the AI-based robot in terms of sensing, judging and actuating. Also this course includes the basic concepts and theory and hands-on techniques required for the students who participates in ‘the AI-based Human-Machine collaboration’ program. | ||
| ME7122 | Robot Mechanism | |
| This course introduces the mechanisms of robot manipulators and actuators. We study engineering methods to design and implement robot mechanisms, and analyze their underlying dynamics. The course also explores the mechanisms of continuum robots and soft robots. Students will learn how to apply these mechanisms to a variety of applications. | ||
| ME7123 | Industry-University Collaborative Project | |
| In this course, graduate students who participate in ‘AI-robot-based human-machine collaboration expert train program’ conduct Industry-University Collaborative Project in the area of AI-robot related robotics, human-machine collaboration, factory automation and etc. | ||
| ME775 | Advanced Automatic Control | |
| Consider the overall contents of the automatic control and study the general topic in application of the automtic control. Increase ability of application amd realization of control for the real system using Matlab/Simulink and Arduino Mega controller | ||
| ME776 | Mobile robotics | |
| The objective of this course is to provide the basics required to develop autonomous mobile robots. Both hardware (energy, locomotion, sensors, embedded electronics, system integration) and software (real-time programming, signal processing, control theory, localization, trajectory planning, high-level control) aspects will be tackled. Theory will be deepened by exercises and application to real robots. | ||
| SWCON7003 | Multi-view Geometry | |
| A basic problem in computer vision is to understand the structure of a real world scene. This course covers relevant geometric principles and how to represent objects algebraically so they can be computed and applied. We will learn epipolar geometry, fundamental matrix, camera calibration, and structure-from-motion. Recent major developments in the theory and practice of 3D scene reconstruction will be handled. | ||
| 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. | ||
| SWCON7025 | Full Stack Deep Learning | |
| Full Stack Deep Learning helps you bridge the gap from training machine learning models to deploying artificial intelligence systems in the real world. This course teaches full-stack production deep learning: Formulating the problem and estimating project cost; Finding, cleaning, labeling, and augmenting data; Picking the right framework and compute infrastructure; Troubleshooting training and ensuring reproducibility; Deploying the model at scale. | ||
| SWCON7026 | Advanced Statistics for Data Science | |
| Statistics is used to process complex problems in the real world so that data scientists and analysts can look for meaning trends and changes. This course helps students learn about statistical analyzing tools and its accurate application. This course focuses on the statistical concepts and tools used to study the association between variables and causal inference. Key concepts include probability distributions, statistical significance, hypothesis testing, and regression. | ||
| 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. | ||
| SWCON7033 | Social System Design and Analysis | |
| We are constantly connecting and communicating with numerous people online. In this course, we will explore various design elements that make up social systems and study social network theories and various social network analysis cases. Through social data collection, analysis, and insight extraction, we can develop the ability to propose more valuable system designs and strategies based on a deep understanding of people's diverse behavioral patterns. |