For more details on the courses, please refer to the Course Catalog
| Code | Course Title | Credit | Learning Time | Division | Degree | Grade | Note | Language | Availability |
|---|---|---|---|---|---|---|---|---|---|
| ESM4013 | Web Information System | 3 | 6 | Major | Bachelor/Master |
3-4
1-4 |
Industrial Engineering | - | No |
| This course deals with the technologies related to the building of web-based information systems. Some of the major topics covered in this course are information systems architecture under web environment, EDI (Electronic Data Interchange), DTD (Document Type Definition), application of XML, web publishing, linking database to the web, automatic retrieval and processing of web information, design and use of intelligent agent, strategic use of web information, and BPR and ERP under web environment. | |||||||||
| ESM4014 | Human Information Processing | 3 | 6 | Major | Bachelor/Master |
3-4
1-4 |
Industrial Engineering | - | No |
| This course provides graduate students with the theories and studies on human information processing, including signal detection theory, human capabilities and limitations in information processing, and ROC curves. The methodological emphasis incorporates a wide variety of mathematical modeling and experimental techniques. | |||||||||
| ESM4025 | Experimental Data Analysis | 3 | 6 | Major | Bachelor/Master |
3-4
1-4 |
Industrial Engineering | - | No |
| Exposition of the philosophy and tools of exploratory data analysis. Tools include graphical techniques, data transformation, robust and resistant summaries, residual analysis, and resampling methods. Application to the analysis real data sets, stressing alternative analysis using software. | |||||||||
| ESM4026 | Discrete Optimization | 3 | 6 | Major | Bachelor/Master |
3-4
1-4 |
Industrial Engineering | - | No |
| In this course we study optimization of discrete structure. We study mathematical theory and application of discrete optimization. We study modeling methods, polyhedral theory, computational complexity, branch-and-bound algorithm, cutting plane algorithm, and approximation theory. We emphasis on practical application and computational algorithm development. We also review current status of research in discrete optimization. | |||||||||
| ESM4027 | Meta Heuristic | 3 | 6 | Major | Bachelor/Master | 1-4 | Industrial Engineering | - | No |
| In this course we study meta heuristic algorithm for hard problems in system optimization. Modeling methods, computational complexity theory, local improvements, simulated annealing, tabu search, and genetic algorithm are studied. The students are going to have experiences in application of a meta heuristic method for his/her own problem. | |||||||||
| ESM4028 | Network Theory and Applications | 3 | 6 | Major | Bachelor/Master | 1-4 | Industrial Engineering | - | No |
| We study graph theory and network modeling in production, manufacturing, and system industries. We also study complex network theory such as random graph, scale-free network, and small world theory. By this course, students can get knowledge and experience for using network modeling & analysis and basic study for future theoretical research. | |||||||||
| ESM4029 | Intelligent Information System | 3 | 6 | Major | Bachelor/Master |
3-4
1-4 |
Industrial Engineering | - | No |
| As the progress of Artificial Intelligence and Expert Systems (AI/ES) technologies, the MIS managers need to understand not only the fundamentals of AI/ES, but also their relationships with the existing information systems technologies such as database and mathematical models. To meet this need, this course teaches the basic concepts and development tools of AI/ES and applies them to the managerial decision support. The discussing cases and performing project are encouraged to learn the real world experience of developing intelligent information systems. This course concentrates on the survey and research of latest AI theories. Area of interest includes reasoning methods, machine learning, understanding planning, case-based reasoning, and neural network. | |||||||||
| ESM4032 | Stochastic Processes | 3 | 6 | Major | Bachelor/Master |
3-4
1-4 |
Industrial Engineering | - | No |
| Theories and analyses of the stochastic processes occurring in industry are covered. | |||||||||
| ESM4035 | Usabillity Engineering | 3 | 6 | Major | Bachelor/Master | 1-4 | Industrial Engineering | - | No |
| Introduction to Usability in Web and Human-Computer Interface design. The course introduces definition, evaluation techniques, measurement details, and case studies. Students can learn how to design Web pages and various products for enhancing usability, and how to evaluate the usability through term projects. | |||||||||
| ESM4036 | Affective Information Processing | 3 | 6 | Major | Bachelor/Master | 1-4 | Industrial Engineering | - | No |
| This course will cover the basic concepts of affective information processing to understand human capability of information processing in visual, auditory, and tactual modalities, and how to utilize this capability to induce human satisfaction in HCI. By examining diverse aspects of human affection in product design, students will gain understandings in the technology acceptance and affection in user experience design. | |||||||||
| ESM4037 | Financial Engineering | 3 | 6 | Major | Bachelor/Master | 1-4 | Industrial Engineering | - | No |
| This course covers investment science and financial engineering for the business corporations. Interest concepts, cash flows, fixed interest assets, interest analysis, mean-variance portfolio, and CAPM are studied. We also studied financial derivatives, option theories, futures, binomial tree model, Black-Scholes modlel. interest rate derivative models. | |||||||||
| ESM4038 | Business Risk Management | 3 | 6 | Major | Bachelor/Master | 1-4 | Industrial Engineering | - | No |
| Any business is full of a variety of risks. A variety of risk management methods will be introduced theoretically, and then be applied to problems in practices including M&A (merger & alliances), R&D investment, etc. Risk-return portfolio management methods are at the center of this class, but some up-to-date methods including VaR(Value at Risk), jump modeling with Langevin equation, etc. will be introduced. | |||||||||
| ESM4039 | Business Information System Analysis and Design | 3 | 6 | Major | Bachelor/Master | 1-4 | Industrial Engineering | - | No |
| This course covers the various methodologies (Structured Analysis and Design, Information Engineering, etc.), techniques (data modeling, process modeling), and tools (CASE, RDBMS, data dictionary) used in developing information systems application. Students are expected to work on a class project to apply what they learned from the course. | |||||||||
| ESM4040 | Analysis of Variance and Regression Analysis | 3 | 6 | Major | Bachelor/Master | 1-4 | Industrial Engineering | - | No |
| This course pursues students' deep understanding in various technical methods in experimental design, ANOVA, and regression analysis. This course particularly emphasizes analytical perspectives of experimental outcome and data through exercises and real case studies. | |||||||||
| ESM4043 | Advanced Topics in SCM | 3 | 6 | Major | Bachelor/Master | 1-4 | Industrial Engineering | - | No |
| The course studies a range of advanced topics about models and applications in supply chain management. A special attention will be given to development of state-of-the-art mathematical models and solution methods to find optimal solutions based on theories in operations research. Topics includes planning production in supply chains, inventory management models, transportation decision in supply chain management, location and distribution decisions in supply chains, supplier selection models, supply chain risk management and global supply chain management. | |||||||||