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 3C
- Academic unit or major
- Center of Data Science and Artificial Intelligence
- Instructor(s)
- Asako Kanezaki / Tsuyoshi Murata / Norio Tomii / Isao Ono / Katsumi Nitta / Kei Miyazaki / Keiji Okumura / Jun Sakuma / Yoshihiro Miyake / Kenji Aoyagi / Shoichi Ohwada / Fumiaki Kobayashi / Masanori Nakagawa / Tomoya Kodama / Jun Deguchi / Yuichiro Yoshinari / Munenobu Hashizume / Kosuke Harada / Naoki Moriguchi / Keiko Nakagawa
- Class Format
- Lecture (HyFlex)
- Media-enhanced courses
- -
- Day of week/Period
(Classrooms) - 7-8 Fri
- Class
- -
- Course Code
- DSA.P433
- 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)
In this course, lectures based on practical experience are given by lecturers of Hitachi Zosen Corp., Kioxia Corp., Dai-ichi Sankyo Corp. Ltd., Takenaka Corp., JERA Corp. and Mitsubishi Corp.
The following lecture schedule has been revised to be more specific (September 10, 2024).
Keywords
Data Science, Artificial Intelligence, Pharmaceutical companies, construction companies, materials, general trading companies
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 | New challenges in the manufacturing industry, about digital transformation | Hitachi Zosen, a manufacturing company that has been in business for more than 140 years, will take on a new challenge as of October 1, 2011 under the company name Canadevia. This year's event will provide an opportunity for participants to think about the future challenges of the manufacturing industry while explaining the need for DX and digital transformation in particular. |
| Class 2 | AI/DX Applications in Semiconductor Storage and Memory Development and Manufacturing to Support Generative AI | Semiconductor storage supporting generative AI - Trends in generative AI (explosion of model size) - Challenges in computation-driven AI (learning cost, black box problem) - Memory retrieval AI (concept, future of generative AI) AI/DX applications in memory development and manufacturing - Issues in semiconductor manufacturing - Big data generated by factories - AI applications using big data (yield monitoring, automatic defect classification, equipment structure optimization) |
| Class 3 | Discussion of the applicability of data science in life science | This lecture will discuss how data science can contribute to the development of life sciences. |
| Class 4 | Data Science for Drug Development in Pharmaceutical Industry | How data science can contribute to drug development in pharmaceutical industry will be explained in this lecture. |
| Class 5 | AI Development for Promoting Digital Transformation (DX) in Construction Companies | This lecture introduces the process and key points as well as the content of AI development for applying AI in business. |
| Class 6 | Next Generation Power Plants and the Future of Energy Optimization | Students will learn examples of Digital and AI applications for businesses in the power generation business to the electricity market |
| Class 7 | Mitsubishi Corporation's AI and Data Science Business | This presentation will introduce MC's AI strategy and its significance to MC's wide-ranging business operations. |
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.P633 "Progressive Applied Practical Data Science and AI 3C".
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 C1 (XCO.T495-1), which was offered until FY2023. Students who took Practical AI and Data Science C1 as undergraduates should register for this course. Students who took Practical AI and Data Science C1 in graduate school may not register for this course.