シラバスの表示

(IMAC-U)数理情報学演習 / Exercises in Computer-Aided Problem

単位数: 2. 担当教員: 松隈 啓, 岡谷 貴之. 開講年度: 2024.

主要授業科目/Essential Subjects

授業の目的・概要及び達成方法等

Google Classroomのクラスコードは工学部Webページにて確認すること。
学部シラバス・時間割(https://www.eng.tohoku.ac.jp/edu/syllabus-ug.html)

授業の目的・概要及び達成方法等(E)

The class code for Google Classroom can be found on the Web site of
the School of Engineering:
https://www.eng.tohoku.ac.jp/edu/syllabus-ug.html (JP Only)

Students will learn how a computer can be used to solve mathematical problems. Although the course will use MATLAB or Octave for this purpose, its focus is more on mastering mathematical skills rather than learning how to use it.
Starting with the basic usage of Octave (or MATLAB) and how to write a program on it, students will learn how they can solve various mathematical problems by writing and executing simple programs. The first half of each class day will be spent for explaining problems and their solutions. Students will try to solve exercise problems the rest of the time. The course will cover not only mathematics that students have already learned, such as calculus, differential equation, linear algebra, etc., but also those that they have not learned, such as numerical computation, signal processing, statistics, machine learning, etc.
The goal of this course is to have students master skills of solving the specific problems considered in this course using Octave (or MATLAB) and futher obtain a concept of how they can utilize a computer to deal with novel problems.
Google Classroom class code: w6g4lfk

他の授業科目との関連及び履修上の注意(E)

All students are required to bring laptop computers to every class. Students will be guided to install necessary software on the first day of the course.

授業計画(E)

1. Installation and basic usage of software
2. Fundementals of Octave/MATLAB
3. Matrices and linear algebra I
4. Roots of algebraic and transcendental equations
5. Least-square method and line fitting
6. Numerical integration and partial differential equations
7. Signal processing
8. Matrices and linear algebra II
9. Statistics I
10. Statistics II
11. Machine learning I
12. Machine learning II
13. Exercise
14. Exercise
15. Final interview

授業時間外学習

This item is omitted.

授業時間外学習(E)

Preparation: Students must read the handouts etc. distributed on the web pages of this exercise, and look up unknown terms, if any, on specialized books or the Internet to gain minimum understanding.
Review: Students must read back the handouts based on what they have learned in the exercise, create new problems, and then try to solve them.

成績評価方法及び基準(E)

Grading will be based on a weighted combination of class participation, assignments, and interviews.

教科書および参考書

    オフィスアワー(E)

    Anytime

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