Course title
330141002
Numerical Analysis and Computing

 IOKA Eri
Middle-level Diploma Policy (mDP)
Program / Major mDP Goals
IoT Course DP-4a・2 キャリアを見据えた高度な専門知識
情報処理やネットワーキングに関する技術を駆使し、情報社会の基盤となるIoTシステムを開発できる。
Software Course DP-4b・3 専門分野と他分野を関連付ける素養
主軸となる分野の専門知識を他分野と関連付ける分野横断型の知識と行動力を修得し、社会で活用できる。
Media Course DP-4c・3 専門分野と他分野を関連付ける素養
主軸となる分野の専門知識を他分野と関連付ける分野横断型の知識と行動力を修得し、社会で活用できる。
Data Science Course DP-4d・3 専門分野と他分野を関連付ける素養
主軸となる分野の専門知識を他分野と関連付ける分野横断型の知識と行動力を修得し、社会で活用できる。
Mechatronics Course DP-4・3 専門分野と他分野を関連付ける素養
主軸となる分野の専門知識を他分野と関連付ける分野横断型の知識と行動力を修得し、社会で活用できる。
Architecture and Architectural Engineering Course DP-4a・3 専門分野と他分野を関連付ける素養
主軸となる分野の専門知識を他分野と関連付ける分野横断型の知識と行動力を修得し、社会で活用できる。
Environmental Systems and Urban Planning Course DP-4b・3 専門分野と他分野を関連付ける素養
主軸となる分野の専門知識を他分野と関連付ける分野横断型の知識と行動力を修得し、社会で活用できる。
Bioscience Course DP-4a・3 専門分野と他分野を関連付ける素養
主軸となる分野の専門知識を他分野と関連付ける分野横断型の知識と行動力を修得し、社会で活用できる。
Biomedical Engineering Course DP-4b・3 専門分野と他分野を関連付ける素養
主軸となる分野の専門知識を他分野と関連付ける分野横断型の知識と行動力を修得し、社会で活用できる。
Sports Engineering Course DP-4c・3 専門分野と他分野を関連付ける素養
主軸となる分野の専門知識を他分野と関連付ける分野横断型の知識と行動力を修得し、社会で活用できる。
Mathematical Sciences Course DP-4・3 専門分野と他分野を関連付ける素養
主軸となる分野の専門知識を他分野と関連付ける分野横断型の知識と行動力を修得し、社会で活用できる。
Purpose of class
This course aims to equip students with the programming skills required to implement numerical analysis methods, including error analysis (rounding errors and loss of significance), matrix computations, data interpolation, numerical integration, and the numerical simulation of approximate solution models for nonlinear equations and differential equations. The course also provides an introduction to machine learning.
Course description
Advances in computer technology have made it possible to model complex phenomena and perform simulations for prediction, estimation, and evaluation. Simulation-based prediction and evaluation play an essential role in engineering by reducing development costs, improving production efficiency, and minimizing hazardous operations. Accordingly, simulation is an indispensable skill for engineers. This course introduces the fundamental methods of numerical analysis required for simulation, including error analysis (rounding errors and loss of significance), matrix computations, data interpolation, numerical integration, and the numerical solution of nonlinear equations and differential equations. In the latter part of the course, students are also introduced to the fundamentals of machine learning.
Goals and objectives
  1. Students will be able to explain the concepts of numerical errors, including rounding errors, truncation errors, and loss of significance.
  2. Students will be able to understand the principles of numerical analysis and implement algorithms for matrix computations, data interpolation and approximation, numerical integration, Newton's method for nonlinear equations, and the numerical solution of differential equations.
  3. Students will be able to understand the fundamentals of machine learning and implement basic machine learning algorithms using Python.
Relationship between 'Goals and Objectives' and 'Course Outcomes'

 Exercises1 Exercises2 Exercises3 Total.
1. 20% 10% 5% 35%
2. 5% 20% 10% 35%
3. 30% 30%
Total. 25% 30% 45% -
Evaluation method and criteria
Assessment is based on the combined score of programming assignments and short quizzes designed to assess students' achievement of the learning outcomes. All assignments must be completed.
An assignment is considered complete only if both of the following requirements are met by the specified deadline:
・The assignment has been checked and approved by the instructor; and
・The corresponding source code has been submitted.
Submitting only the source code without completing the instructor's check does not constitute completion of the assignment.
A passing grade of 60 points can be achieved by completing all programming assignments, earning at least 60% of the available points for the assignments, and scoring at least 60% on the short quizzes (worth a total of 50 points).
Language
Japanese
Class schedule

 Class schedule HW assignments (Including preparation and review of the class.) Amount of Time Required
1. Course Orientatio Students are expected to read the distributed course materials and complete the assigned exercises before each class, and review the lecture content after each class. 100minutes
2. Numerical Errors and Matrix Computations Students are expected to read the distributed course materials and complete the assigned exercises before each class, and review the lecture content after each class. 120minutes
3. Numerical Methods for Systems of Linear Equations Students are expected to read the distributed course materials and complete the assigned exercises before each class, and review the lecture content after each class. 120minutes
4. Root-Finding Algorithms for Nonlinear Equations Students are expected to read the distributed course materials and complete the assigned exercises before each class, and review the lecture content after each class. 120minutes
5. Exercises #1 Students are expected to read the distributed course materials and complete the assigned exercises before each class, and review the lecture content after each class. 360minutes
6. Interpolation and Approximation #1 Students are expected to read the distributed course materials and complete the assigned exercises before each class, and review the lecture content after each class. 120minutes
7. Interpolation and Approximation #2 Students are expected to read the distributed course materials and complete the assigned exercises before each class, and review the lecture content after each class. 120minutes
8. Numerical Differentiation and Integration Students are expected to read the distributed course materials and complete the assigned exercises before each class, and review the lecture content after each class. 120minutes
9. Exercises #2 Students are expected to read the distributed course materials and complete the assigned exercises before each class, and review the lecture content after each class. 360minutes
10. Numerical Solutions of Ordinary Differential Equations Students are expected to read the distributed course materials and complete the assigned exercises before each class, and review the lecture content after each class. 120minutes
11. Systems of Ordinary Differential Equations Students are expected to read the distributed course materials and complete the assigned exercises before each class, and review the lecture content after each class. 120minutes
12. Exercises #3 Students are expected to read the distributed course materials and complete the assigned exercises before each class, and review the lecture content after each class. 360minutes
13. Introduction to Machine Learning #1 Students are expected to read the distributed course materials and complete the assigned exercises before each class, and review the lecture content after each class. 120minutes
14. Introduction to Machine Learning #2 Students are expected to read the distributed course materials and complete the assigned exercises before each class, and review the lecture content after each class. 120minutes
Total. - - 2380minutes
Feedback on exams, assignments, etc.
ways of feedback specific contents about "Other"
Feedback in/outside the class.
Textbooks and reference materials
None. Course materials will be distributed as needed during class.
Prerequisites
None
Office hours and How to contact professors for questions
  • Wed.：12:40〜13:10
Regionally-oriented
Non-regionally-oriented course
Development of social and professional independence
  • Course that cultivates an ability for utilizing knowledge
Active-learning course
More than one class is interactive
Course by professor with work experience
Work experience Work experience and relevance to the course content if applicable
N/A N/A
Education related SDGs:the Sustainable Development Goals
  • 9.INDUSTRY, INNOVATION AND INFRASTRUCTURE
Last modified : Tue Aug 04 04:01:18 JST 2026