대학원 과정

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.

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.

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.

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.

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.

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

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.

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.

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.

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.

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.

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.

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.

Reinforcement learning is a promising area in modern artificial intelligence research. Its recent development based on computation power leads to enormous applications, including robotics, finance, healthcare, and business administration. This course will deal with an introduction to the field of optimal control and reinforcement learning, and students will learn about the mathematical elements and computational approaches. At the end of this course, students will understand core theories and algorithms in modern reinforcement learning research.

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.

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.

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.

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.

This course introduces graduate students with current and special topics in Industrial Engineering.

This course introduces graduate students with current and special topics in Industrial Engineering.

This course introduces graduate students with current and special topics in Industrial Engineering.

This course introduces graduate students with current and special topics in Industrial Engineering.

This course introduces graduate students with current and special topics in Industrial Engineering.