For more details on the courses, please refer to the Course Catalog
| Code | Course Title | Credit | Learning Time | Division | Degree | Grade | Note | Language | Availability |
|---|---|---|---|---|---|---|---|---|---|
| ESM5109 | Patents and Entrepreneurship1 | 3 | 6 | Major | Master/Doctor | 1-4 | Korean,Korean | Yes | |
| The important issues and process of filing patents will be lectured in this class. Also, entrepreneurship using patents will be studied. The related laws will be reviewed, and actual case studies will be carried out. | |||||||||
| ESM5114 | Competitive Management Strategy | 3 | 6 | Major | Master/Doctor | - | No | ||
| Theories and methods to reflect customer needs, product functionality, manufacturability, economics and other core factors in new product development process are covered. The critical parameters of design, machining, and assembly stages are defined, and theories/methods are covered to develop manufacturing processes based on the parameters. Also, various risks to hinder successful development and responding strategies to the risks are introduced. | |||||||||
| ESM5118 | Mathematical Programming for Data Mining 1 | 3 | 6 | Major | Master/Doctor | Korean | Yes | ||
| This course provides a review and understanding on the numerical optimization algorithms in the context of data mining and machine learning applications. Through case studies on text classification and the training of deep neural networks, we discuss how optimization problems arise in data mining and machine learning and what makes them challenging. A major theme of this course is that large-scale data mining represents a distinctive setting in which the stochastic gradient (SG) method has traditionally played a central role while conventional gradient-based nonlinear optimization techniques typically falter. Based on this viewpoint, this course discuss a comprehensive theory of a straightforward, yet versatile SG algorithm, discuss its practical behavior, and highlight opportunities for designing algorithms with improved performance. This leads to a discussion about the next generation of optimization methods for large-scale machine learning, including an investigation of two main streams of research on techniques that diminish noise in the stochastic directions and methods that make use of second-order derivative approximations. This course includes; -Data Mining Case Studies, Overview of Optimization Methods, Analyses of Stochastic Gradient Methods, Noise Reduction Methods, Second-Order Methods. | |||||||||
| ESM5119 | Mathematical Programming for Data Mining 2 | 3 | 6 | Major | Master/Doctor | - | No | ||
| This course provides a mathematical programming approaches to data mining, machine learning, and neural networks. In addition to providing a general overview, motivating the importance of data mining problems within the area of knowledge discovery in databases, our aim is to list some of the pressing research challenges, and outline opportunities for contributions by the optimization research communities. Towards these goals, this course study formulations of the basic categories of data mining methods as optimization problems. We also provide examples of successful mathematical programming approaches to some data mining problems. Data Mining is rapidly evolving areas of research that are at the intersection of several disciplines, including statistics, databases, pattern recognition/AI, optimization, visualization, and high-performance and parallel computing. This course outlines the basic notions in this area, defines some of the key ideas and problems, and motivates their importance. The main goal of this course is to outline areas to which the optimization research community can make significant contributions. Towards this end, this course provides high-level coverage of this area with specific formulations of some of the basic problems in data mining as mathematical programming problems. | |||||||||
| ESM5120 | Learning from Data | 3 | 6 | Major | Master/Doctor | - | No | ||
| This course provides an elementary introduction to modern techniques for learning from data. Students will learn the theories and applications of machine learning and deep learning, including feed-forward neural networks, convolutional neural networks, recurrent neural networks, auto-encoders, and deep generative models. | |||||||||
| ESM5121 | UX Management | 3 | 6 | Major | Master/Doctor | - | No | ||
| This course examines the concept and practical implications of User Experiences, particularly the life cycle of the UX, from how UX is designed and consumed to how it affects user behaviors. Students will learn the concept of experiences first, and then develop understandings on how human experiences are formed through cognitive process and on what types of experiences exist. The course will focus on UX management and strategies of companies in product and service designs in the later part of the course. | |||||||||
| ESM5200 | Independent Research for University and Industry Collaboration in Smart FactoryⅡ | 3 | 6 | Major | Master/Doctor | Korean | Yes | ||
| The students will participate in a collaborative research project among industry, university and research institution in Smart Factory, and conduct practical independent research related to design and operation of smart factory, or development of software solutions. | |||||||||
| ESM5202 | Smart Factory Supply Chain Management | 3 | 6 | Major | Master/Doctor | - | No | ||
| This course will introduce a concept of a supply chain that consists of suppliers, manufactures, warehouses, retailers, and customers, and techniques and strategies for managing constant flows of materials, funds, and information among stages in a supply chain. | |||||||||
| ESM5203 | Big Data for Business Intelligence | 3 | 6 | Major | Master/Doctor | - | No | ||
| This course provides an introduction to the technologies and techniques for organizing, analyzing, visualizing, and presenting data about business operations in a way that creates business value, and prepares students to be knowledgeable producers and consumers of business intelligence. During the course, students will study a variety of business decisions that can be improved by analyzing large volumes of data about customers, sales, operations, and business performance. Students will employ commercially available business intelligence software to organize, summarize, visualize, and analyze data sets and make recommendations to decision makers based on the results. The course explores the technical challenges of conducting analytics on various forms of data including social media data and the managerial challenges of creating value from business intelligence expertise deployed in organizations. The course includes business cases, in-class discussion, hands-on analyses of business data, and methods for presenting results to decision makers. It is designed for any student interested in analyzing data to support business decision-making, including students whose primary focus is Management Information Systems, Marketing, Operations and Industrial Engineering, Business, Management Engineering, Data Science, or Computer Science. | |||||||||
| ESM5205 | Learning from Big Data | 3 | 6 | Major | Master/Doctor | - | No | ||
| This course provides an elementary introduction to modern techniques for learning from big data. Students will learn the theories and applications of machine learning and deep learning, including feed-forward neural networks, convolutional neural networks, recurrent neural networks, graph neural networks, autoencoders, and deep generative models. | |||||||||
| ESM5206 | Creative Ideation Seminar | 3 | 6 | Major | Master/Doctor | - | No | ||
| The purpose of this course is to enhance the capability of creative ideation in engineering and management. The majors topics include basic theory of creativity, blocks to creativity, creative problem solving process, problem finding and definition, idea generation, idea evaluation and implementation. | |||||||||
| ESM5207 | Quality Management Seminar | 3 | 6 | Major | Master/Doctor | - | No | ||
| Seminar on major topics in quality management. It includes quality costs, quality improvement, quality function deployment, quality engineering, control charts, sampling inspection, standardization and quality assurance, product liability, service quality, six sigma breakthrough strategy, management quality. | |||||||||
| ESM5208 | Fundamentals of Operations Management | 3 | 6 | Major | Master/Doctor | - | No | ||
| The purpose of this course is to understand the planning and control of production system. The majors topics include scientific management, total productive maintenance, Toyota production system, capacity planning, aggregate planning, operations scheduling, inventory management, MRP system, demand forecasting, supply chain management, project management, operations strategy. | |||||||||
| ESM5209 | Productivity Analysis | 3 | 6 | Major | Master/Doctor | - | No | ||
| Productivity Analysis is a multi-disciplinary field that spans theoretical and applied research addressing the measurement, analysis, and improvement of productivity from the perspectives of economics, management sciences, operations research, and public administration. Topics covered in this course will include basic concepts related to the measurement of productivity (criteria, objectives, benchmark, production frontier, etc.). Classic data envelopment analysis (DEA) techniques will be discussed during the course, together with the modern network/dynamic/intertemporal settings. The course will be supported by practical exercises and class discussions. | |||||||||
| ESM5212 | Prognostics and Health Management | 3 | 6 | Major | Master/Doctor | - | No | ||
| This course introduces the concepts and methods of PHM (Prognostics and Health Management) with case studies. PHM involves sensing, diagnosis, prognosis and health management modules. This course discusses the state-of-the-art method and algorithm review, as well as representative industrial applications for each module. | |||||||||