교과목


교과목

  • IE201 Operations ResearchⅠ [계량경영학Ⅰ]
    Operations Research is a quantitative approach to decision making based on the scientific method of problem solving. This course is an introduction to the key aspects of Operations Research methodology. Students will learn how to model and solve a variety of deterministic problems using optimization techniques. Topics will include basic theory, model formulation, solution techniques, and result analysis/interpretation.
  • IE207 Statistical Computing [통계계산]
    The aim of this course is to understand how to conduct statistical tests, analysis, and inference using Python programming language. At the end of the course, students will be able to construct efficient algorithms for statistical procedures without relying on built-in functions.
  • IE209 Operations Management [생산운영관리]
    Operations Management is concerned with the advancement of production and delivery of goods and services in industries. In this course, we will learn and apply concepts and methods in Operations Management, including forecasting, optimal production and delivery, quality engineering, supply chain management, and product/service system design.
  • IE303 Data Mining[데이터마이닝]
    This course gives you understanding of fundamental concepts, techniques, and algorithms in data mining, beginning with topics such as linear regression, classification and clustering, and ending up with association rule mining and recommender systems. Students are strongly encouraged to identify and solve real-world industrial problems using data mining techniques.
  • IE305 Operations Research Ⅱ [계량경영학 Ⅱ]
    Operations Research II is the second part of a two-course sequence of Operations Research that develops/analyzes models commonly used in the analysis of complex decision-making problems. This course will extend the course materials discussed in Operations Research I and will introduce students to several important types of mathematical and stochastic (probabilistic) models and solution techniques, including dynamic programming, stochastic processes, queueing models, inventory control, supply chain management, and revenue management.
  • IE308 Service Intelligence [서비스 지능]
    Service systems in transportation, retail, healthcare, entertainment, hospitality, and other areas are configurations of people, information, organizations, and technologies that operate together for specific functions and values. The field of Service Science is emerging for the study of complex service systems, and involves methods and theories from a range of disciplines, including operations, industrial engineering, marketing, computer science, psychology, information systems, design, and more. In this course, we will learn and apply concepts and methods in Service Science for service management and engineering. In particular, we will focus on the application of artificial intelligence to service management and engineering.
  • IE313 Time-series Analysis [시계열 분석]
    This course introduces the basics of modern time series analysis. Students will learn about the characteristics of time series data and the basics of time series regression and exploratory data analysis. Then, we will cover various models and techniques in time series analysis including ARMA/ARIMA models, spectral analysis and filtering, and state space models. In addition, some additional topics including GARCH models or artificial neural network (ANN) models would be briefly introduced if time allows. The analyses will be performed using Python.
  • IE314 Investment Science [계량투자론]
    This course introduces the basic knowledge on various financial instruments as well as quantitative models for finance. The main topics include: equities, fixed-income securities, derivatives including options and futures, asset pricing models, and investment management.
  • IE361 Quantitative Technology Management [계량기술경영]
    Technology management is a set of management disciplines that allows organizations to manage their technological fundamentals to create competitive advantage. This course will cover a variety of topics and quantitative methods in the field of technology management. Students are expected to learn the ways of integrating data science into different types of problems in the field of technology management.
  • IE362 Statistical Quality Management [통계적 품질관리]
    The objective of this course is to teach various methods that can be used for improving the quality of products and processes. Topics for this course are quality system requirements, designed experiments, process capability analysis, measurement capability, statistical process control, and acceptance sampling plans.
  • IE404 Data-driven Process Management [데이터 기반 프로세스 관리]
    Business processes are ubiquitous in modern organizations and their execution is increasingly supported by advanced information systems, which make available a large amount data related to their design and execution. The first part of this course focuses on the typical phases of business process management in an organisation, that is, business process identification, business process modelling (using BPMN 2.0), and business process analysis and improvement. The second part focuses on process mining, that is, a state of the art technique to extract knowledge about business processes, e.g., process models, from the logs of the IT systems supporting their execution.
  • IE406 Applied Machine Learning [기계학습 응용]
    This is an undergraduate level course in applied machine learning, which is designed for juniors or seniors. The primary emphasis will be learning how machine learning algorithms can be applied to solve complex real-world problems. At the end of the course, students will be able to (1) learn about basic and advanced machine learning, including deep learning, (2) identify various real-world problems for the use of machine learning, and (3) employ machine learning algorithms to solve the real-world problems in various fields.
  • IE408 Principles of Deep Learning [딥러닝 원론]
    The 21st century has been the golden era of machine learning, and the GPU-based deep learning algorithms becomes an indispensable tool in both science and engineering. This course introduces the fundamentals of deep learning and its applications to image and sequential data. The objective of this course is to help students to understand the elements of deep learning, from the basic operations to the advanced architectures in the recent AI research, in the view of statistics and computer science. Topics for the course include convolutional networks, recurrent neural networks, and attention mechanisms.
  • IE412 Advanced Investment Science [고급계량투자론]
    Financial planning of individuals or institutions involves identifying investors’ financial goals and liabilities, providing optimal investment and consumption plans, and overseeing the actual implementation of financial plans. In this course, we study the process of financial planning, relevant theories including modern portfolio theory and asset-liability management, and relevant optimization and machine learning techniques. In addition, we will learn how to implement various financial planning problems using Python.
  • IE421 Blockchain Systems [블록체인 시스템]
    This course introduces blockchain technology. The objective of this course is to cover the basics of blockchain technology as a technology for designing and implementing cross-organisational information systems. The course starts with an overview of blockchain technology and its emergence in the field of cryptocurrency and then will focus more extensively on designing systems using blockchain. The course will look both at applications of blockchain in real world scenarios and at the more technical aspects related with the implementation of such systems.
  • IE422 Social Network Analysis [사회 연결망 분석]
    This is an undergraduate level course in social network analysis, which is designed for juniors or seniors. This course introduces students to the basic concepts and analysis techniques in (online) social network analysis. Students will develop modeling and analysis skills using online user-generated data, especially in social network analysis. At the end of the course, students will be able to (1) learn the fundamentals of network theory, (2) understand the basic concepts of social network analysis, (3) analyze large-scale online user-generated data on social networks (e.g., social media, such as Facebook or Twitter), and (4) apply machine learning techniques to discover knowledge from online social networks.
  • IE450 Project Lab. [프로젝트 랩]
    Students and strategic partners from industry will work in project teams and undertake management engineering industrial projects. The teams must aim to disseminate completed project outcomes to industry. The progress of each project will be reviewed based on formal presentations
  • IE470 Special Topics in MGEⅠ [MGE 특론Ⅰ]
    This course is designed to discuss contemporary topics in Management Engineering. Actual topics and cases will be selected by the instructor and may vary from term to term.
  • IE471 Special Topics in MGE Ⅱ [MGE 특론Ⅱ]
    This course is designed to discuss contemporary topics in Management Engineering. Actual topics and cases will be selected by the instructor and may vary from term to term.
  • IE472 Special Topics in MGE Ⅲ [MGE 특론 Ⅲ]
    This course is designed to discuss contemporary topics in Management Engineering. Actual topics and cases will be selected by the instructor and may vary from term to term.
  • IE690 Master's Research [석사논문연구]
    This course is related with the students graduate thesis and dissertation. As such, students should be actively working in a laboratory setting and gaining experience through hands-on experimentation.
  • IE890 Doctoral's Research [박사논문연구]
    This course is related with the students graduate thesis and dissertation. As such, students should be actively working in a laboratory setting and gaining experience through hands-on experimentation.
  • IE502 Statistical Inference [통계적 추론]
    This course will provide students with analytical and decision making skills through a variety of topics in statistics and optimization modeling. Underlying theory for statistical analysis and its business applications will be emphasized. This helps students evaluate and handle business situations with statistics in mind. As a result, students will be well prepared to describe and analyze data for decision makings in business fields such as marketing, operations, and finance. This course aims to teach students programming techniques for managing, and summarizing data, and reporting results.
  • IE503 Pattern Recognition and Machine Learning [패턴인식 및 기계학습]
    This course gives you better understanding of many concepts, techniques, and algorithms in machine learning, beginning with topics such as classification and linear regression and ending up with more state-of-the art topics such as deep learning. The course will give the student the basic ideas and intuition behind modern machine learning methods as well as a bit more formal understanding of how, why, and when they work. The underlying theme in the course is statistical inference as it provides the foundation for most of the methods covered.
  • IE505 Linear Programming [선형계획법]
    This course introduces linear programming (LP) and its extensions emphasizing the underlying mathematical structures. Students will learn convexity, LP theory, simplex method, simplex method with bounded variables, Karush-Kuhn-Tucker conditions, duality, economic interpretation of dual variables, post-optimality analysis, Dantzig-Wolfe decomposition, computational analysis in LP, and interior point algorithms. Relevant LP applications and state-of-the-art optimization software will also be presented. Upon completion of this course, students should understand and interpret LP formulations, and furthermore, be able to design their own models and apply various mathematical techniques to solve optimization problems.
  • IE506 Supply Chain Management [공급망관리]
    Derived from domestic and global competition, firms in many industries seek to create innovative ways to move products from raw materials through the manufacturing process to customers more efficiently and effectively. Such innovation has been facilitated by the development of information technology. The firms redesign their supply chains to collect, process, transmit, share, and use a large amount of information with efficacy. Still others are focusing on cooperative relationships among all the players in the value chain and bypassing unneeded stages. This course examines many of the recent innovations in this area with an emphasis on technologies
  • IE507 Convex Optimization [컨벡스 최적화]
    Most engineering problems incorporate optimal decision makings, i.e., optimization. In this course, students will learn the importance of efficient formulations of optimization problems and how to model real problems efficiently via convex optimization theory. A large part of this course will be related to mathematical foundation of convex optimization. Also, there will be programming exercises to solve some example engineering problems.
  • IE508 Knowledge Service Engineering [지식서비스공학]
    Service systems in transportation, retail, healthcare, entertainment, hospitality, and other areas are configurations of people, information, organizations, and technologies that operate together for specific functions and values. One difficulty in engineering and managing complex service systems is the lack of data required to monitor and improve the system elements. However, with recent advances in sensing technologies, various kinds and massive amounts of data can be collected from the elements of the service system, such as people and physical objects. This advancement contributes to unlocking the limitations of engineering and managing service systems. In this course, we will learn and apply concepts and methods for engineering and management of service systems with various types of data. Through assignments and a term project, the students will develop their own cases of service systems engineering and management.
  • IE509 Advanced Quality Control [고급 품질관리]
    The objective of this course is to teach fundamental methods about anomaly and change detection in a process or an environment. Topics covered include the univariate and multivariate analysis for continuous and discrete data, risk adjustments, data pre-analyses (such as dimension reduction), and scan statistics. This course is designed for master's students in the engineering and statistics fields to learn about anomaly and change detections in terms of the basic concepts and practical tools. Also, it will help doctoral students in both fields broaden their knowledge base and get exposed to new applications.
  • IE510 Smart Factory & Advanced Manufacturing [스마트 공장 및 고급 제조업]
    Production is more than manufacturing – it encompasses everything from R&D to design, consumer behavior and end-of-use cycles. Emerging technologies are transforming the world of production, enabling more efficient processes and creating new value for industry, society and the environment.Smart Factory is a mechanism enhancing manufacturing innovation. Korea and other global leading countries are accelerating the progress of smart factory. The class will review strategy of smart factory and compares with other global leading countries. This class brings together key enablers such as IIOT, AI/Cognitive, Big Data, Advanced Robotics, DPS/Digital Twin, 3-D printing, and Cloud can transform the industry and studies various case study to accelerate inclusive technology while stimulating innovation, sustainability and employment. The class evaluates pilot study examining the latest approaches in skills development, drives improvements in partnerships and informs business model transformations and next generation industrial development strategies.
  • IE511 Introduction to Deep Learning [딥러닝개론]
    The 21st century has been the golden era of machine learning, and the GPU-based deep learning algorithms becomes an indispensable tool in both science and industry. This course introduces the fundamentals of deep learning. The objective of this course is to help students to understand the basic operation and architecture in modern AI research. Topics for the course include convolutional networks, recurrent neural networks, and attention mechanisms.
  • IE512 Technology Management: An IE Perspective [기술경영]
    This course will cover the latest research trends in technology management (TM) from an IE perspective. The course materials are research papers published in the prestigious journals such as Research Policy, Technovation, Technological Forecasting and Social Change, and R&D Management. This course consists of two parts. In the first part, we will discuss research streams in the field of TM. In the second part, we will intensively study major models and methods that have been widely employed in TM literature. Students are expected to develop independent research capabilities including identification of research opportunities, building sound theoretical base, and choice of rigorous methodologies.
  • IE513 Neural Network Learning Theory [신경망 학습이론]
    This course will introduce the learning theory of neural networks. We will start with basic principles such as empirical risk minimization, and study the relationship between error, sample size, and model complexity. Especially, we will learn how to represent the complexity of neural networks in terms of the covering number and VC dimension. We will also cover efficient learning methods.
  • IE514 Reinforcement Learning [강화학습]
    This course will introduce the learning theory of neural networks. We will start with basic principles such as empirical risk minimization, and study the relationship between error, sample size, and model complexity. Especially, we will learn how to represent the complexity of neural networks in terms of the covering number and VC dimension. We will also cover efficient learning methods.
  • IE515 Causal Learning & Explainable AI [인과학습 & 설명가능 AI]
    In data science, it is essential to understand the causal relationship between variables as well as a high-performance prediction based on correlation. Causal learning is an emerging area in the machine learning, statistics, and artificial intelligence community. In this course, we will provide concepts, mathematical principles, and algorithms to deal with causal inference and causal discovery problems. Students will learn how to combine data and domain knowledge for causal reasoning, which is crucial in decision making science, e.g. medicine, education, and business administration.
  • IE516 Predictive Process Analytics [예측 프로세스 분석]
    This course covers the fundamentals of predictive monitoring using business process event logs. Event log are a particular type of data that capture the execution of business processes in organisations. They can be used to build predictive models of aspects of interests about the execution of processes, such as predicting the remaining execution time, the next activity that will be executed, or the outcome of the process (e.g., whether a given service level objective will be satisfied or not). The course covers mainly the design and implementation of business process predictive monitoring models built using machine learning techniques. As such, it focuses on topics such as feature extraction and engineering from event logs and encoding of event log information. In the second part, the course will also discuss techniques for anomaly detection in event logs. This is an emerging topic in the literature that deals with developing methods to automatically clean event logs from anomalies that can be introduced by problems with logging, i.e., system malfunctioning or human resource manual errors. More in detail, after participating into this course, students will:
    • Understand event logs and the formats in which they can be available;
    • Design and implement feature extraction techniques from event logs to train predictive models;
    • Understand and implement different techniques for encoding information in event logs;
    • Design, implement and evaluate predictive models of business processes using event logs and basic machine learning techniques;
    • Understand different paradigms for the design of anomaly detection techniques for event logs;
    • Design and implement simple anomaly detection models for event logs.
  • IE517 Manufacturing System Design & Simulation [생산 시스템 설계 및 시뮬레이션]
    By the end of this course, students will: 1) Understand how the manufacturing systems have developed and what it would look in the future, 2) Be able to specify a manufacturing system by investigating the problems in process planning, process control, and cost analysis, and 3) Be able to model and simulate manufacturing system in a shop-floor level with the simulation methods.
  • IE518 3D Printing [3D 프린팅]
    This course aims to help undergraduate students to understand the contemporary issues on additive manufacturing technology and its applications. The students will survey related literatures, discuss the pros and cons of the technology, and identify applicational cases of the 3D printing technology. Having successfully completed this course, the student will be able to:
    • Understand the basic technologies of 3D printing and their applications.
    • Perform digital design of conceptual parts, direct manufacturing of it using 3D printing, and surface finishing for better quality.
  • IE551 Special Topics in IE I IE [특론 I]
    This course introduces graduate students with current and special topics in Industrial Engineering.
  • IE552 Special Topics in IE II IE [특론 II]
    This course introduces graduate students with current and special topics in Industrial Engineering.
  • IE553 Special Topics in IE III IE [특론 III]
    This course introduces graduate students with current and special topics in Industrial Engineering.
  • IE554 Special Topics in IE Ⅳ IE [특론 Ⅳ]
    This course introduces graduate students with current and special topics in Industrial Engineering.
  • IE555 Special Topics in IE Ⅴ IE 특론 [Ⅴ]
    This course introduces graduate students with current and special topics in Industrial Engineering.