2024 Special graduate degree programs Specially Offered Degree Programs for Graduate Students Tokyo Tech Academy for Convergence of Materials and Informatics
Materials Informatics
- Academic unit or major
- Tokyo Tech Academy for Convergence of Materials and Informatics
- Instructor(s)
- Masakazu Sekijima / Nobuaki Yasuo / Kazuaki Kuwahata
- Class Format
- Lecture (HyFlex)
- Media-enhanced courses
- -
- Day of week/Period
(Classrooms) - 1-4 Fri
- Class
- -
- Course Code
- TCM.A404
- Number of credits
- 200
- Course offered
- 2024
- Offered quarter
- 4Q
- Syllabus updated
- Mar 14, 2025
- Language
- English
Syllabus
Course overview and goals
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 knowledge and basic skills for handling large-scale data sets with the aid of computers.
Course description and aims
Students will be able to apply basic knowledge on the statistical machine learning for analyzing data and evaluating the obtained results mathematically.
Keywords
machine learning, classification, regression, clustering, dimensionality reduction, training/generalization errors, model selection
Competencies
- Specialist skills
- Intercultural skills
- Communication skills
- Critical thinking skills
- Practical and/or problem-solving skills
Class flow
Basically, students will attend classes face-to-face. Classes and exercises are held alternately.
Course schedule/Objectives
| Course schedule | Objectives | |
|---|---|---|
| Class 1 | 12/6 Fri. Class guidance | Learn the abstract of mathematical/statistical knowledge in data science. |
| Class 2 | 12/6 Fri. Exercise: basic Python exercises | Understand the general idea of computation on python, and obtain basic knowledge for this course. |
| Class 3 | 12/13 Fri. Fundamentas of data analysis | Understand the outline of statistics and data science. |
| Class 4 | 12/13 Fri. Exercise: computing environment and warming-up of programming | Learn how to use Python libraries and Google Colaboratory for data mining. |
| Class 5 | 12/27 Fri. Classification and model selection | Understand the simple classification rule generation mechanism. Learn the idea of training/generalization error and model selection method. |
| Class 6 | 12/27 Fri. Exercise: Classification and model selection | Understand the principle idea of the decision tree and how to use it. |
| Class 7 | 1/10 Fri. Clustering | Understand the idea of unsupervised learning and clustering algorithm. |
| Class 8 | 1/10 Fri. Exercise: Clustering | Understand the mechanism of clustering and how to apply to sample data. |
| Class 9 | 1/24 Fri. Principal component analysis | Understand the mechanism of principal component analysis and its mathematical background. |
| Class 10 | 1/24 Fri. Exercise: Principal component analysis | Learn how to apply principal component analysis to sample data. |
| Class 11 | 1/31 Fri. Dimensionality reduction | Learn dimensionality reduction methods to map high dimensional data into low dimensional space. |
| Class 12 | 1/31 Fri. Exercise: dimensionality reduction | Learn how to apply dimensionality reduction methods to sample data. |
| Class 13 | 2/7 Fri. Ensemble learning | Understand the mechanism of ensemble learning and its major methods. |
| Class 14 | 2/7 Fri. Exercise: Ensemble learning | Learn how to apply ensemble learning methods to sample data. |
Study advice (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 afterward (including assignments) for each class.
They should do so by referring to course materials.
Textbook(s)
Not specified
Reference books, course materials, etc.
Distributed via T2SCHOLA or Zoom.
Evaluation methods and criteria
Based on reports of classes/exercises and final 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)
Prerequisites
It is better to have basic knowledge of linear algebra, analysis, and statistics.
Office hours
Questions can be sent by email (at any time).
Other
* Only TAC-MI students and those who have passed the eligibility screening for Graduate Major in Materials and Information Sciences can register this course in this year.
* Students who are not eligible are recommended to take XCO.T487: Fundamentals of Data Science and XCO.T488: Exercises of Fundamentals of Data Science, which have the same content as this course.
* This course has the same content as the combination of XCO.T487: Fundamentals of Data Science and XCO.T488: Exercises of Fundamentals of Data Science, and cannot be taken in duplicate.
* XCO.T489: fundamentals of artificial intelligence and XCO.T490: exercises of fundamentals of artificial intelligence are recommended to be taken.
* Students are required to get Google accounts and to get ready for using Google Colaboratory.