Mathematical Engineering Area
With the development of computers, it has become possible to manage a large amount of information in a short time. On the other hand, more advanced mathematical analysis methods are required to present problems, and provide solutions based on this information. In the Mathematical Engineering area, students learn various engineering tools (models) that are powerful weapons in the practice of “proposing scientific management methods”, which is the purpose of management science and engineering. They learn the basic theory of each model through class sessions, and become able to utilize it as “practical knowledge” through seminars.
| Course name | Course description | Target year |
|---|---|---|
| Seminar on Mathematical Engineering | The goal of this course is to establish the basic knowledge of various engineering tools (models) acquired in each course of the Mathematical Engineering area as “usable” knowledge through exercise problem-solving and practical training. | 2 – 4 |
| Mathematical Optimization | This course will deal with some themes in mathematical programming (such as linear programming, nonlinear programming, graph theory, and combination optimization), and provide an overview of typical algorithms and basic theories. | 2 – 4 |
| Applied Probability | This course will outline the basics of probability theory and Markov chains. It will mainly explain: probability space, random variables, probability distribution, conditional probability, expected values, conditional expected values, simultaneous probability distribution, convergence of random variables, law of large numbers, central limit theorem, and Markov chain. | 2 – 4 |
| Mathematical Statistics | In this course, students will acquire a basic knowledge of mathematical statistics using multivariate data through applied methods and applications. | 2 – 4 |
| Discrete Mathematics | This course will give introductory/general lectures on discrete mathematics and combinatorics, which are the basis of modeling/analysis of various discrete systems and information processing technology in policy and planning sciences. | 2 – 4 |