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- School of Science
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School of Engineering
- Undergraduate major in Mechanical Engineering
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- 工学院，物質理工学院，環境・社会理工学院共通科目
- Liberal arts and basic science courses
- First-Year Courses
- School of Science
- School of Engineering
- School of Materials and Chemical Technology
- School of Computing
- School of Life Science and Technology
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School of Environment and Society
- Department of Architecture and Building Engineering
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- Graduate major in Technology and Innovation Management
- Common courses
- Liberal arts and basic science courses
- Academic unit or major
- School of Computing
- Instructor(s)
- Yamada Takuji Yamashita Yukihiko Kelly Shane Tomobe Haruka Terazawa Yuki Kishimoto Maki Hasegawa Kei Uchida Makoto Yanagisawa Keisuke Murata Tsuyoshi Nitta Katsumi Nagahashi Hiroshi Kobayashi Takao Miyake Yoshihiro
- Class Format
- Exercise
- Media-enhanced courses
- Day/Period(Room No.)
- Tue7-8()
- Group
- -
- Course number
- XCO.T678
- Credits
- 1
- Academic year
- 2021
- Offered quarter
- 4Q
- Syllabus updated
- 2021/9/17
- Lecture notes updated
- -
- Language used
- Japanese
- Access Index
-
Course description and aims
In the current society, it is essential in all fields to appropriately exploit "big data" for finding rules and/or making predictions/decisions. This course aims to help students to manipulate computer software tools for data analysis to get new
findings.
Student learning outcomes
Students will be able to understand the basis of data processing mechanisms and make use of various data analysis software tools appropriately.
Keywords
classification, clustering, principal component analysis, dimension reduction, training/generalization errors, cross validation
Competencies that will be developed
| ✔ Specialist skills | Intercultural skills | Communication skills | Critical thinking skills | ✔ Practical and/or problem-solving skills |
Class flow
In class, students are required to solve exercise problems that are linked with the contents of taught course ``XCO.T487
Fundamentals of data science".
Course schedule/Required learning
| Course schedule | Required learning | |
|---|---|---|
| Class 1 | Prerequirement exam | Check basic knowledge about mathematics and Python language |
| Class 2 | Arrangement of computing environment and warming-up of programming | Arrange computing environment and carry out simple exercises of programming |
| Class 3 | Classification | Do exercises on methods for extracting discrimination rules from labeled data |
| Class 4 | Principal component analysis | Do exercises on principal component analysis with mathematical issues related to it |
| Class 5 | Clustering | Do exercises on methods for categorizing unlabeled data into several categories |
| Class 6 | Dimension reduction | Do exercises on methods for dimension reduction such as multidimensional scaling and canonical correlation analysis |
| Class 7 | Ensemble learning | Do exercises on methods for ensemble learning |
Out-of-Class Study Time (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)
None
Reference books, course materials, etc.
Based on reports for given assignments.
Assessment criteria and methods
Based on reports for given assignments.
Related courses
- XCO.T677 ： Fundamentals of progressive data science
- XCO.T483 ： Advanced Artificial Intelligence and Data Science A
- XCO.T485 ： Advanced Artificial Intelligence and Data Science C
- XCO.T486 ： Advanced Artificial Intelligence and Data Science D
- XCO.T679 ： Fundamentals of progressive artificial intelligence
- XCO.T490 ： Exercises in fundamentals of artificial intelligence
Prerequisites (i.e., required knowledge, skills, courses, etc.)
Only doctor course students can register this exercise.
When you apply this exercise, take "XCO.T677 Fundamentals of advanced data science'' , "XCO.T679 Fundamentals of advanced artificial Intelligence" and "T680 Exercises in fundamentals in advanced artificial intelligence" of the same quarter of the same year in parallel. If there are many applicants, a lottery may be held.
Other
Exercises are carried out using Google Colaboratory. Students are required to get Google accounts and to get ready for usingfunctions of "fi le upload/download" in Google Drive.
