Credit Requirements

이수학점 정보
Department Major Double Major Minor
R E Total R E Total R E Total
Department of Industrial Engineering 24 24 48 15 21 36 9 9 18


전공필수 정보
Course Code Course Title Major Double Minor Cred-Lect-Exp Remark Semester
IE201 Operations Research Ⅰ 3-3-0 [PRE]
IE207 Statistical Computing 3-3-0 2
IE209 Industrial Operations Management 3-3-0 1
IE303 Data Mining 3-3-0 1
IE305 Operations Research Ⅱ 3-3-0 [PRE] IE201, IE209 1
IE313 Time-series Analysis 3-3-0 [PRE] MTH211 2
IE404 Data-driven Process Management 3-3-0 2
IE406 Applied Machine Learning 3-3-0 [PRE] IE303, MTH211 1
IE450 Project Lab. 3-1-4 1

※ Students with major/double major must complete ‘Project Lab(3)’ as a required course.
※ ‘Project Lab’ course not required for Minor can be counted as Free Elective course.


전공선택 정보
Course Code Course Title Major Double Minor Cred-Lect-Exp Remark Semester
IE308 Service Intelligence 3-3-0 [PRE] IE209
IE361 Quantitative Technology Management 3-3-0
IE362 Statistical Quality Management 3-3-0 1
IE408 Principles of Deep Learning 3-3-0 [PRE] ITP117, IE303 1
IE412 AI for Finance 3-3-0 1
IE421 Blockchain Systems 3-3-0
IE422 Social Network Analysis 3-3-0 [PRE] IE303 2
IE470 Special Topics in IE Ⅰ 3-3-0
IE471 Special Topics in IE Ⅱ 3-3-0
IE472 Special Topics in IE Ⅲ 3-3-0
UNI202 Blockchain and Cryptocurrencies  ○  ○  ○ 1-1-0 1
UNI205 Dynamic Programming and its Applications 1-1-0 Winter
BME206 Cognitive Neuroscience 3-3-0
BME222 Introduction to Human Factor Engineering 3-3-0
BME310 Experimental Design 3-3-0
CSE362 Artificial Intelligence 3-3-0
CSE364 Software Engineering 3-3-0
CSE463 Machine Learning 3-3-0
ECHE350 AI-driven Design of Energy Materials and Process 3-3-0
MEN201 Computational Tools for Engineers 3-3-0
MEN301 Numerical Analysis 3-2-2
MEN353 Manufacturing System Design & Simulation 3-3-0
MEN455 3D Printing 3-3-0
MEN491 Creating Autonomous Car
MGT315 Econometrics 3-3-0
MTH251 Mathematical Analysis Ⅰ 3-3-0
MTH321 Numerical Analysis 3-3-0
MTH333 Scientific Computing 3-3-0
MTH342 Probability 3-3-0
MTH344 Mathematical Statistics 3-3-0
MTH361 Mathematical Modeling and Applications 3-3-0
MTH421 Introduction to Partial Differential Equations 3-3-0
MTH461 Stochastic Processes 3-3-0
UEE206 Science Humanities 3-3-0
UNI203 Design and implementation of data-driven machine 1-1-0

※ [PRE]: Prerequisite, [IDEN]: Identical

Curriculum Change

History of course change 2021
No. Course
IE201 Operations Research Ⅰ
No Prerequisite
IE314 Investment Science
IE412 Advanced Investment Science
Prerequisite : IE314
History of course change 2022
No. Course
IE201 Operations Research Ⅰ

Prerequisite : MTH203

IE314 <Closed>

Substitution : IE412

IE412 AI for Finance

No Prerequisite

UNI205 Dynamic Programming and its Applications
  • 1Operations Research Ⅰ

    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.

  • 2Statistical Computing

    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.

  • 3Operations Management

    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.

  • 4Data Mining

    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.

  • 5Operations Research 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.

  • 6Service Intelligence

    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.

  • 7Time-series Analysis

    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.

  • 8Investment Science

    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.

  • 9Quantitative Technology Management

    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.

  • 10Statistical Quality Management

    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.

  • 11Data-driven Process Management

    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.

  • 12Applied Machine Learning

    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.

  • 13Principles of Deep Learning

    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.

  • 14Advanced Investment Science

    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.

  • 15Blockchain Systems

    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.

  • 16Social Network Analysis

    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.

  • 17Project Lab.

    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

  • 18Special Topics in MGEⅠ

    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.

  • 19Special Topics in MGEⅡ

    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.

  • 20Special Topics in MGE 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.

Graduation Requirements

· For Liberal Arts and Leadership requirements, refer to school Common requirements.

졸업 이수요건 정보
Category Credits Remarks Subtotal
Basic Required 17 Calculus 1(3), General Physics I(3), General Chemistry I(3), General Biology(3), Introduction to AI Programming I(3), General Chemistry Lab I(1), General Physics Lab I(1) (Total 17 credits) At least 32 Credits
Elective 15 Complete 15 credits including required courses Required: Applied Linear Algebra(3), Statistics(3), AIP2(3)
Major Required 24 Refer to Required course list below
· Must include Project Lab (3 credits)
At least 48 Credits
Elective 24 Refer to Elective course list below At least 48 Credits
Internship 3 Internship (Choose one among Research, Industrial, Venture Creation, Co-op) 3 Credits
Free Elective 17 All courses accepted At least 17 Credits

Basic Requirements

· ● Required○ Elective ◑ Recommended

기초 이수요건 정보
No. Course Code. Course Title Credits Major Double Major Minor
15 credits 15 credits 15 credits
1 MTH112 CalculusⅡ 3
2 PHY103 General PhysicsⅡ 3
3 CHM102 General ChemistryⅡ 3
4 PHY108 General Physics Lab Ⅱ 1
5 CHM106 General Chemistry LabⅡ 1
6 MTH201 Differential Equations 3
7 MTH203 Applied Linear Algebra 3
8 MTH211 Statistics 3
9 MGT102 Entrepreneurship 3
10 IE101 Introduction to Data Science 3
11 ITP117 Introduction to AI Programming Ⅱ 3
12 ITP111 Probability & Random Process 3
13 ITP112 Discrete Mathematics 3
14 UNI108 Understanding Major
Industrial Engineering Relay Seminar