学部・大学院区分
Undergraduate / Graduate
工・博前
時間割コード
Registration Code
2848502
科目区分【日本語】
Course Category
専門科目
科目区分【英語】
Course Category
Specialized Courses
科目名 【日本語】
Course Title
[G30]システムダイナミックス特論
科目名 【英語】
Course Title
[G30]Advanced Lectures on System Dynamics
コースナンバリングコード
Course Numbering Code
担当教員 【日本語】
Instructor
未定 ○
担当教員 【英語】
Instructor
Undetermined ○
単位数
Credits
2
開講期・開講時間帯
Term / Day / Period
春 木曜日 5時限
Spring Thu 5
授業形態
Course style
講義
Lecture
学科・専攻【日本語】
Department / Program
機械システム工学専攻 自動車工学プログラム
学科・専攻【英語】
Department / Program
Department of Mechanical Systems Engineering Automotive Engineering Graduate Program
必修・選択【日本語】
Required / Selected
必修・選択【英語】
Required / Selected


授業の目的 【日本語】
Goals of the Course(JPN)
Substantial difficulties of dynamic systems in the real world lie in the involvement of a large number of related factors that deviate statistically. Multivariate analyses and statistics are common tools for understanding and modeling these intricate systems. This course is arranged for those who had few opportunities to study statistics, multivariate analyses, and some basis for these mathematics. We learn intermediate topics of classic multivariate analyses and related statistics. We also practice the methods of multivariate analysis on real data and interpret the results throughout the course.
授業の目的 【英語】
Goals of the Course
Substantial difficulties of dynamic systems in the real world lie in the involvement of a large number of related factors that deviate statistically. Multivariate analyses and statistics are common tools for understanding and modeling these intricate systems. This course is arranged for those who had few opportunities to study statistics, multivariate analyses, and some basis for these mathematics. We learn intermediate topics of classic multivariate analyses and related statistics. We also practice the methods of multivariate analysis on real data and interpret the results throughout the course.
到達目標 【日本語】
Objectives of the Course(JPN))
Substantial difficulties of dynamic systems in the real world lie in the involvement of a large number of related factors that deviate statistically. Multivariate analyses and statistics are common tools for understanding and modeling these intricate systems. This course is arranged for those who had few opportunities to study statistics, multivariate analyses, and some basis for these mathematics. We learn intermediate topics of classic multivariate analyses and related statistics. We also practice the methods of multivariate analysis on real data and interpret the results throughout the course.
到達目標 【英語】
Objectives of the Course
Substantial difficulties of dynamic systems in the real world lie in the involvement of a large number of related factors that deviate statistically. Multivariate analyses and statistics are common tools for understanding and modeling these intricate systems. This course is arranged for those who had few opportunities to study statistics, multivariate analyses, and some basis for these mathematics. We learn intermediate topics of classic multivariate analyses and related statistics. We also practice the methods of multivariate analysis on real data and interpret the results throughout the course.
バックグラウンドとなる科目【日本語】
Prerequisite Subjects
Mathematics, especially, linear algebra and statistics of undergraduate level.
バックグラウンドとなる科目【英語】
Prerequisite Subjects
Mathematics, especially, linear algebra and statistics of undergraduate level.
授業の内容【日本語】
Course Content
1-2 h: Multivariate regression analysis
3 h: Outlier analysis
4-5 h: Principal component analysis
6 h: Factor analysis
7-8 h: Discrimination analysis
9-10 h: Structural equation modeling
11 h: Covariance selection
12 h: Time-series analysis
13 h: Preparation of final presentation
14 h: Youtube presentation and marking by all students
15 h: Honorable presentations by selected speakers
授業の内容【英語】
Course Content
1-2 h: Multivariate regression analysis
3 h: Outlier analysis
4-5 h: Principal component analysis
6 h: Factor analysis
7-8 h: Discrimination analysis
9-10 h: Structural equation modeling
11 h: Covariance selection
12 h: Time-series analysis
13 h: Preparation of final presentation
14 h: Youtube presentation and marking by all students
15 h: Honorable presentations by selected speakers
成績評価の方法と基準【日本語】
Course Evaluation Method and Criteria
Three reports (60%) and one presentation (40%) are collectively evaluated. All students have to prepare for the final presentation, for which real world data are examined with one of the analysis methods.
成績評価の方法と基準【英語】
Course Evaluation Method and Criteria
Three reports (60%) and one presentation (40%) are collectively evaluated. All or selected students have to prepare for the final presentation, for which real world data are examined with one of the analysis methods.
履修条件・注意事項【日本語】
Course Prerequisites / Notes
The lectures will be delivered on Youtube. The URLs will be announced every week by e-mails registered in NUCT. Final presentations will be held by Microsoft Teams.
履修条件・注意事項【英語】
Course Prerequisites / Notes
The lectures will be delivered on Youtube. The URLs will be announced every week by e-mails registered in NUCT. Final presentations will be held by Microsoft Teams.
教科書【日本語】
Textbook
Available on the course website:
http://www.mech.nagoya-u.ac.jp/asi/ja/lecture/okamoto_system.html
教科書【英語】
Textbook
Available on the course website:
http://www.mech.nagoya-u.ac.jp/asi/ja/lecture/okamoto_system.html
参考書【日本語】
Reference Book
Provided through NUCT.
参考書【英語】
Reference Book
Provided through NUCT.
授業時間外学習の指示【日本語】
Self-directed Learning Outside Course Hours
授業時間外学習の指示【英語】
Self-directed Learning Outside Course Hours
使用言語【英語】
Language used
使用言語【日本語】
Language used
授業開講形態等【日本語】
Lecture format, etc.
授業開講形態等【英語】
Lecture format, etc.
遠隔授業(オンデマンド型)で行う場合の追加措置【日本語】
Additional measures for remote class (on-demand class)
遠隔授業(オンデマンド型)で行う場合の追加措置【英語】
Additional measures for remote class (on-demand class)