2026 (Current Year) Faculty Courses School of Environment and Society Undergraduate major in Civil and Environmental Engineering
Computers and Fundamental Programming B
- Academic unit or major
- Undergraduate major in Civil and Environmental Engineering
- Instructor(s)
- Hitomu Kotani / Ayako Akutsu
- Class Format
- Lecture/Exercise (Face-to-face)
- Media-enhanced courses
- -
- Day of week/Period
(Classrooms) - 1-2 Tue (南6号館2F 219演習室)
- Class
- -
- Course Code
- CVE.M303
- Number of credits
- 0.50.50
- Course offered
- 2026
- Offered quarter
- 2Q
- Syllabus updated
- Apr 23, 2026
- Language
- Japanese
Syllabus
Course overview and goals
Numerical analysis using computer is now important and essential skill for various fields. In this class, computer language Fortran 90/95, which is especially used in large-scale numerical computing, is used. By understanding basic grammar of the computer language and algorithms of major numerical-analysis methods, which are commonly used in research fields, basic programing skill will be acquired.
Through this course, students who don’t have any programming experience are expected to understand algorithms of major numerical-analysis methods and to be able to make basic program for numerical analysis.
Course description and aims
By the end of this class, students will be able to:
(1) understand basic grammars for programming
(2) understand algorithms of major numerical analysis methods required in research and development
(3) write basic programs for numerical analysis of phenomena according to their own needs, and
(4) understand and implement the basic concepts and algorithms of optimization (linear and nonlinear programming) and statistical inference (EM and MCMC algorithms) and be able to deal with specific problems
Keywords
numerical analysis, algorithm, Fortran, programming, numerical optimization, statistical inference
Competencies
- Specialist skills
- Intercultural skills
- Communication skills
- Critical thinking skills
- Practical and/or problem-solving skills
Class flow
Basics of programming and algorithms is trained through both lectures and exercises with using terminal of GSIC.
Course schedule/Objectives
| Course schedule | Objectives | |
|---|---|---|
| Class 1 | Lecture and exercise: Theory and implementation of linear programming |
Understand and implement linear programming algorithms. |
| Class 2 | Lecture: Theory of nonlinear programming |
Understand nonlinear programming algorithms. |
| Class 3 | Exercise: Implementation of nonlinear programming |
Implement nonlinear programming algorithms. |
| Class 4 | Lecture and exercise: Theory and implementation of statistical inference (EM algorithm) |
Understand and implement algorithms of statistical inference (EM algorithm) |
| Class 5 | Lecture: Theory of statistical inference (MCMC algorithm) |
Understand algorithms of statistical inference (MCMC algorithm) |
| Class 6 | Exercise: Implementation of statistical inference (MCMC algorithm) |
Implement algorithms of statistical inference (MCMC algorithm). |
| Class 7 | Project and Q&A |
Programming for the final project and Q&A etc. |
Study advice (preparation and review)
To enhance effective learning, students are encouraged to spend approximately 100 minutes preparing for class and another 100 minutes reviewing class content afterwards (including assignments) for each class.
They should do so by referring to textbooks and other course material.
Textbook(s)
Noting
Reference books, course materials, etc.
Handouts will be distributed before the beginning of class via T2SCHOLA.
Following textbook is recommended but will not be used in the course:
金谷 健一：これなら分かる最適化数学: 基礎原理から計算手法まで、共立出版、2005
小西 貞則・越智 義道・大森 裕浩：計算統計学の方法―ブートストラップ・EMアルゴリズム・MCMC (シリーズ予測と発見の科学 5)、朝倉書店、2008
花田 政範・松浦 壮：ゼロからできるMCMC、講談社、2020
Evaluation methods and criteria
Learning achievement is evaluated by combining results from reports.
Related courses
- CVE.M301 ： Computers and Fundamental Programming A
- CVE.M302 ： Computers and Applied Programming
Prerequisites
Completing CVE.M301 or same level as CVE.M301 is strongly recommended.