▶Japanese
Post to SNS
- Academic unit or major
- Tokyo Tech Academy for Convergence of Materials and Informatics
- Instructor(s)
- Sekijima Masakazu Kabashima Yoshiyuki Matsushita Yu-Ichiro Kosugi Taichi Yasuo Nobuaki
- Class Format
- Lecture
- Media-enhanced courses
- Day/Period(Room No.)
- Fri1-4(南4号館 情報ネットワーク演習室 第１演習室, Information Network Exercise Room 1st Exercise Room)
- Group
- -
- Course number
- TCM.A405
- Credits
- 2
- Academic year
- 2019
- Offered quarter
- 4Q
- Syllabus updated
- 2019/11/29
- Lecture notes updated
- -
- Language used
- English
- 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 gives fundamental knowledges and basic skills for handling large-scale data sets with the aid of computers.
Student learning outcomes
Students will be able to apply basic knowledges on statistics for analyzing data and evaluating the obtained results mathematically.
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 each week we give a lecture and an exercise session.
Course schedule/Required learning
| Course schedule | Required learning | |
|---|---|---|
| Class 1 | Class guidance | Guidance for class flow, computing environment, and used programming language (Python) |
| Class 2 | Prerequirement exam - data mining, and - basic statistics and mathematics for data analysis | Check basic knowledge about mathematics and Python language |
| Class 3 | Fundamentals of data analysis | Learn basic knowledge about statistics and data science |
| Class 4 | Arrangement of computing environment and warming-up of programming | Arrange computing environment and carry out simple excercises of programming |
| Class 5 | Classification and model evaluation | Learn methods for extracting discrimination rules from labeled data. Learn about difference between training error and generalization error, and methods of model evaluation. |
| Class 6 | Classification | Do exersises on methods for extracting discrimination rules from labeled data |
| Class 7 | Clustering | Learn methods for categorizing unlabeled data into several categories |
| Class 8 | Clustering | Do exersises on methods for categorizing unlabeled data into several categories |
| Class 9 | Principal component analysis | Learn principal component analysis together with mathematical issues related to it |
| Class 10 | Principal component analysis | Do exersises on principal component analysis with mathematical issues related to it |
| Class 11 | Dimension reduction | Learn methods for dimension reduction such as multidimensional scaling and canonical correlation analysis |
| Class 12 | Dimension reduction | Do exersises on methods for dimension reduction such as multidimensional scaling and canonical correlation analysis |
| Class 13 | Advanced topics | Learn methods for ensemble learning |
| Class 14 | Advanced topics | Do exersises on methods for ensemble learning |
| Class 15 | General discussion | Discuss possible applications of data analysis in various fields |
Textbook(s)
Not specified
Reference books, course materials, etc.
Distributed via OCW-i
Assessment criteria and methods
Based on reports for given assignments.
Related courses
- XCO.T489 ： Fundamentals of artificial intelligence
- XCO.T490 ： Exercises in fundamentals of artificial intelligence
- XCO.T483 ： Advanced Artificial Intelligence and Data Science A
- XCO.T484 ： FinTech and Data Science
- XCO.T485 ： Advanced Artificial Intelligence and Data Science C
- XCO.T486 ： Advanced Artificial Intelligence and Data Science D
- TCM.A403 ： Materials simulation (I)
- TCM.A404 ： Materials Informatics (R)
Prerequisites (i.e., required knowledge, skills, courses, etc.)
Take a prerequirement exam on "linear algebra", "analysis", and "basic grammar and functions of Python3" in the first class.
Other
Only TAC-MI students can register this course in 2019. Students are required to get Google accounts and to get ready for using functions of "file upload/download" in Google Drive.
