2024 Special graduate degree programs Specially Offered Degree Programs for Graduate Students Center of Data Science and Artificial Intelligence
Applied Practical Data Science and Artificial Intelligence 3B
- Academic unit or major
- Center of Data Science and Artificial Intelligence
- Instructor(s)
- Asako Kanezaki / Norio Tomii / Tsuyoshi Murata / Isao Ono / Kei Miyazaki / Keiji Okumura / Jun Sakuma / Katsumi Nitta / Yoshihiro Miyake / Zaixing Mao / Masahiro Akiba / Takaaki Miyauchi / Emiko Kamasaki / Yoshitaka Okazaki / Takashi Handa / Katsuhisa Yoshida / Masanori Shimada / / / Takuya Nanri / / Hironori Tanji / Kei Furukawa / Shimpei Takemoto
- Class Format
- Lecture (HyFlex)
- Media-enhanced courses
- -
- Day of week/Period
(Classrooms) - 7-8 Wed
- Class
- -
- Course Code
- DSA.P432
- Number of credits
- 100
- Course offered
- 2024
- Offered quarter
- 3Q
- Syllabus updated
- Mar 14, 2025
- Language
- Japanese
Syllabus
Course overview and goals
The purpose of this class course is to understand the current status and state-of-the-art of social implementation of AI and data science technologies, and to examine the applicability and challenges of these technologies. In each class, lecturers from companies in various fields such as architecture, IT, finance, and materials will introduce case studies of technology and product development using data science and AI.
The goal is for students to gain a broad perspective of the real world by acquiring knowledge about the application of data science and AI technologies in a wide range of fields, and by explaining their considerations about social applications in their assigned reports.
This course emphasizes dialogue with corporate lecturers. In addition to the seven class sessions, students shall, in principle, attend the DS&AI Forum to be held on the afternoon of December 2, 2024 at the Oookayama Campus. (Added on September 10, 2024)
Course description and aims
This course aims to develop ability of each student to be more successful in the real world with the consideration of social implementation of data science and artificial intelligence.
Student learning outcomes
実務経験と講義内容との関連 (又は実践的教育内容)
This course will be taught by lecturers from Daiwa House Inc., Mizuho Financial Group Inc., Shimizu Corp., NGK Insulators Ltd, Resonac Corp., Nissan Motor Corp. and Topcon Corp. based on their practical experience.
The following lecture schedule has been revised to be more specific (September 10,2024).
Keywords
Data Science, Artificial Intelligence, FinTech, Manufacturing, Construction, Machine Learning, Data Utilization, New Business Development, auto mobile, chemical manufacturer
Competencies
- Specialist skills
- Intercultural skills
- Communication skills
- Critical thinking skills
- Practical and/or problem-solving skills
Class flow
This course is classified as a high-flex type, but can only be taken in designated classrooms in Ookayama and Suzukakedai.
Course schedule/Objectives
| Course schedule | Objectives | |
|---|---|---|
| Class 1 | The History of Construction DX and the Digital Human Resources Needed | Learn about digital technologies used in society and the skills required |
| Class 2 | Financial Data Analytics in Practice | Machine learning, statistical science and genetative AI are increasingly being used in banks and other financial institutions. This lecture will explain the characteristics and approaches of data analytics in the financial domain. Challenges and issues to be addressed in the future will also be explained, focusing on the technical aspects. |
| Class 3 | AI and Data Application in the Construction Industry | Learn the importance of digitalization through examples of AI and data utilization initiatives. |
| Class 4 | Promotion of DX at NGK | Understanding of DX promotion activities and data utilization in the manufacturing industry. |
| Class 5 | Semiconductor Industry and Informatics Application in Its Materials Development | Enhancement of International Industrial Competitiveness Directly through The Power of Data Science |
| Class 6 | Domain Knowledge informed Neural Networks ~Beyond Data Collection~ | Learn the importance of understanding the principles behind the subject through the construction of domain knowledge informed neural networks |
| Class 7 | Sensor & AI Experience: Medical & Construction Fields | Experience AI programs for eyecare & infrastructure using latest sensors |
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 afterwards (including assignments) for each class.
Textbook(s)
None required.
Reference books, course materials, etc.
Materials will be provided on T2SCHOLA in advance.
Evaluation methods and criteria
No final exam will be given. Grades will be evaluated based on each assignment report and the participation report of the DS&AI Forum scheduled for December 2. Please note that it is not possible to submit assignment reports for missed lectures. Even if a student submits an assignment report for a lecture he/she has missed, it will not be graded. (Added on September 10, 2024)
Related courses
- XCO.T487 ： Fundamentals of data science
- XCO.T488 ： Exercises in fundamentals of data science
- XCO.T489 ： Fundamentals of artificial intelligence
- XCO.T490 ： Exercises in fundamentals of artificial intelligence
Prerequisites
Doctoral students must take DSA.P632 "Progressive Applied Practical Data Science and AI 3B". In this course, students may be required to bring a PC into the classroom and practice using Google Colab.
Contact information (e-mail and phone) Notice : Please replace from ”[at]” to ”@”(half-width character).
Asako Kanezaki, Katsumi Nitta, Norio Tomii
lecture_ap[at]dsai.titech.ac.jp
Office hours
Contact by e-mail in advance to schedule an appointment.
Other
・This class is a technical course that can be considered an entrepreneurship course. The GAs that this subject corresponds to are GA0M and GA1M (added March 29, 2024).
・This course corresponds to Practical AI and Data Science C2 (XCO.T495-2), which was offered until FY2023. Students who took Practical AI and Data Science C2 as undergraduates should register for this course. Students who took Practical AI and Data Science C2 in graduate school may not register for this course.