NEWS OVERVIEW TARGET STUDENTS LIST OF SUBJECTS REQUIREMENTS TIMETABLE GUIDE RELATED SITES SITE POLICY
NEWS
| Mar 26, 2024 | If you want to apply for this progressive graduate minor, please read "Request for approval: Registration for Progressive Graduate Minor" in page 5 of "How to register/add/cancel courses" and finish Web registration during application period (from Thursday, April 4 to Friday, April 19). Actual procedures are as follows. Access 【Submitting Course Administration Forms】 on the "Web System for Students and Faculty". Submit your request form 【Form No. 15】. (No printed form is required.) *You don't have to fill in "Note" box. *Your request will be approved by faculty in charge of your intended program once it is approved by your academic supervisor. |
| Mar 26, 2024 | We have renewed our website to this site starting from the first semester of FY2024. Accordingly, the old website was discontinued. |
| Mar 17, 2023 | "Progressive Graduate Minor in Data Science and Artificial Intelligence" and "University-wide Education Program in Data Science and Artificial Intelligence" are different educational programs. This site is about the former. About the latter, please see Center of Data Science and Artificial Intelligence. |
OVERVIEW
The dramatic advances in advanced information technology (advanced IT), such as artificial intelligence and data science, continue to rapidly expand the industrial domains that can take advantage of these technologies. Under these circumstances, the shortage of human resources for advanced IT is expanding, and to address this shortage, the Cabinet approved the "Integrated Innovation Strategy" on June 15, 2018, with the goal of establishing a system capable of training and hiring tens of thousands of advanced IT personnel by 2025.
Against this social backdrop, the Progressive Graduate Minor in Data Science and Artificial Intelligence will strongly promote the development of advanced IT human resources who can contribute to solving social issues, in close collaboration with industry and in cooperation with companies and other organizations that support this program. In this program, students will receive high-level basic education from Tokyo Tech faculty members, including mathematical background and programming skills related to artificial intelligence and data science, and at the same time learn a variety of applied methods from business people. In addition, as a comprehensive science and technology university, Tokyo Tech is home to students with diverse professional backgrounds, and by establishing such a program for the development of advanced IT human resources in the graduate school and opening it to all courses, it will be possible to create connections that transcend disciplinary boundaries and develop human resources who can co-create solutions to problems.
By introducing such a human resource development program that is unique to Tokyo Tech, we aim to build a platform for Tokyo Tech to contribute to solving social issues in advanced IT, such as data science and artificial intelligence.
TARGET STUDENTS
Master students, doctoral students and professional degree students of Tokyo Institute of Technology are the target students of this program. Students of all schools and courses are allowed. Students who do not apply for this program are also allowed to study the following twenty six subjects.
Fundamentals of data science, Exercises in fundamentals of data science, Fundamentals of artificial intelligence, Exercises in fundamentals of artificial intelligence, Fundamentals of Progressive Data Science, Exercises in Fundamentals of Progressive Data Science, Fundamentals of Progressive Artificial Intelligence, Exercises in Fundamentals of Progressive Artificial Intelligence, Applied Practical Data Science and Artificial Intelligence 1A, B, C / 2A, B, C / 3A, B, C, Progressive Applied Practical Data Science and Artificial Intelligence 1A, B, C / 2A, B, C / 3A, B, C
LIST OF SUBJECTS
| Division | Code | Subject | Unit | Compulsory subject |
|---|---|---|---|---|
| Specialized subjects (400 series) | XCO.T487 | 基盤データサイエンス Fundamentals of data science | 1-0-0 | A |
| XCO.T488 | 基盤データサイエンス演習 Exercises in fundamentals of data science | 0-1-0 | A | |
| XCO.T489 | 基盤人工知能 Fundamentals of artificial intelligence | 1-0-0 | A | |
| XCO.T490 | 基盤人工知能演習 Exercises in fundamentals of artificial intelligence | 0-1-0 | A | |
| MCS.T403 | 統計的学習理論 Statistical Learning Theory | 2-0-0 | ||
| MCS.T410 | 応用確率論 Applied Probability | 2-0-0 | ||
| MCS.T412 | 情報可視化 Information Visualization | 2-0-0 | ||
| MCS.T418 | 実践的並列コンピューティング Practical Parallel Computing | 2-0-0 | ||
| ART.T459 | 自然言語処理 Natural Language Processing | 2-0-0 | ||
| ART.T462 | 複雑ネットワーク Complex Networks | 2-0-0 | ||
| ART.T463 | コンピュータグラフィクス Computer Graphics | 2-0-0 | ||
| CSC.T438 | 分散アルゴリズム Distributed Algorithms | 2-0-0 | ||
| DSA.P411 | 応用実践データサイエンス･AI第一A Applied Practical Data Science and Artificial Intelligence 1A | 1-0-0 | B | |
| DSA.P412 | 応用実践データサイエンス･AI第一B Applied Practical Data Science and Artificial Intelligence 1B | 1-0-0 | B | |
| DSA.P413 | 応用実践データサイエンス･AI第一C Applied Practical Data Science and Artificial Intelligence 1C | 1-0-0 | B | |
| DSA.P421 | 応用実践データサイエンス･AI第二A Applied Practical Data Science and Artificial Intelligence 2A | 1-0-0 | B | |
| DSA.P422 | 応用実践データサイエンス･AI第二B Applied Practical Data Science and Artificial Intelligence 2B | 1-0-0 | B | |
| DSA.P423 | 応用実践データサイエンス･AI第二C Applied Practical Data Science and Artificial Intelligence 2C | 1-0-0 | B | |
| DSA.P431 | 応用実践データサイエンス･AI第三A Applied Practical Data Science and Artificial Intelligence 3A | 1-0-0 | B | |
| DSA.P432 | 応用実践データサイエンス･AI第三B Applied Practical Data Science and Artificial Intelligence 3B | 1-0-0 | B | |
| DSA.P433 | 応用実践データサイエンス･AI第三C Applied Practical Data Science and Artificial Intelligence 3C | 1-0-0 | B | |
| Specialized subjects (500 series) | MCS.T507 | 統計数理 Theory of Statistical Mathematics | 2-0-0 | |
| MCS.T506 | 計算機支援数理 Mathematical Models and Computer Science | 2-0-0 | ||
| ART.T543 | バイオインフォマティクス Bioinformatics | 2-0-0 | ||
| ART.T548 | 先端人工知能 Advanced Artificial Intelligence | 2-0-0 | ||
| ART.T547 | マルチメディア情報処理論 Multimedia Information Processing | 2-0-0 | ||
| CSC.T521 | クラウドコンピューティングと並列処理 Cloud Computing and Parallel Processing | 2-0-0 | ||
| CSC.T526 | 高性能科学技術計算 High Performance Scientific Computing | 2-0-0 | ||
| CSC.T525 | 先端情報セキュリティ Advanced Information Security | 2-0-0 | ||
| Specialized subjects (600 series) | XCO.T677 | 基盤データサイエンス発展 Fundamentals of Progressive Data Science | 1-0-0 | A |
| XCO.T678 | 基盤データサイエンス発展演習 Exercises in Fundamentals of Progressive Data Science | 0-1-0 | A | |
| XCO.T679 | 基盤人工知能発展 Fundamentals of Progressive Artificial Intelligence | 1-0-0 | A | |
| XCO.T680 | 基盤人工知能発展演習 Exercises in Fundamentals of Progressive Artificial Intelligence | 0-1-0 | A | |
| DSA.P611 | 応用実践 データサイエンス･AI発展第一A Progressive Applied Practical Data Science and Artificial Intelligence 1A | 1-0-0 | B | |
| DSA.P612 | 応用実践 データサイエンス･AI発展第一B Progressive Applied Practical Data Science and Artificial Intelligence 1B | 1-0-0 | B | |
| DSA.P613 | 応用実践 データサイエンス･AI発展第一C Progressive Applied Practical Data Science and Artificial Intelligence 1C | 1-0-0 | B | |
| DSA.P621 | 応用実践 データサイエンス･AI発展第二A Progressive Applied Practical Data Science and Artificial Intelligence 2A | 1-0-0 | B | |
| DSA.P622 | 応用実践 データサイエンス･AI発展第二B Progressive Applied Practical Data Science and Artificial Intelligence 2B | 1-0-0 | B | |
| DSA.P623 | 応用実践 データサイエンス･AI発展第二C Progressive Applied Practical Data Science and Artificial Intelligence 2C | 1-0-0 | B | |
| DSA.P631 | 応用実践 データサイエンス･AI発展第三A Progressive Applied Practical Data Science and Artificial Intelligence 3A | 1-0-0 | B | |
| DSA.P632 | 応用実践 データサイエンス･AI発展第三B Progressive Applied Practical Data Science and Artificial Intelligence 3B | 1-0-0 | B | |
| DSA.P633 | 応用実践 データサイエンス･AI発展第三C Progressive Applied Practical Data Science and Artificial Intelligence 3C | 1-0-0 | B |
REQUIREMENTS
From the list of Special Specialized Courses, 8 credits must be obtained including 4 from selective compulsory subjects (A) and 2 or more from selective compulsory subjects (B) (master students: excluding the subjects that are compulsory in his/her Department / Graduate major, doctoral students: excluding the subjects that were compulsory in his/her master's Department / Graduate major).
From the following four pairs of eight selective compulsory subjects (A), at least one from each must be taken.
- Fundamentals of Data Science, Fundamentals of Progressive Data Science
- Exercises in Fundamentals of Data Science, Exercises in Fundamentals of Progressive Data Science
- Fundamentals of Artificial Intelligence, Fundamentals of Progressive Artificial Intelligence
- Exercises in Fundamentals of Artificial Intelligence, Exercises in Fundamentals of Progressive Artificial Intelligence
From the following nine pairs of eighteen elective compulsory subjects (B), at least one from two or more pairs must be taken.
- Applied Practical Data Science and Artificial Intelligence 1A, Progressive Applied Practical Data Science and Artificial Intelligence 1A
- Applied Practical Data Science and Artificial Intelligence 1B, Progressive Applied Practical Data Science and Artificial Intelligence 1B
- Applied Practical Data Science and Artificial Intelligence 1C, Progressive Applied Practical Data Science and Artificial Intelligence 1C
- Applied Practical Data Science and Artificial Intelligence 2A, Progressive Applied Practical Data Science and Artificial Intelligence 2A
- Applied Practical Data Science and Artificial Intelligence 2B, Progressive Applied Practical Data Science and Artificial Intelligence 2B
- Applied Practical Data Science and Artificial Intelligence 2C, Progressive Applied Practical Data Science and Artificial Intelligence 2C
- Applied Practical Data Science and Artificial Intelligence 3A, Progressive Applied Practical Data Science and Artificial Intelligence 3A
- Applied Practical Data Science and Artificial Intelligence 3B, Progressive Applied Practical Data Science and Artificial Intelligence 3B
- Applied Practical Data Science and Artificial Intelligence 3C, Progressive Applied Practical Data Science and Artificial Intelligence 3C
As the temporary measures, for Applied Artificial Intelligence and Data Science A, B, C, D, Progressive Applied Artificial Intelligence and Data Science A, B, C, D taken before AY2023, the following courses will be considered as having been completed and a determination of completion will be made.
| Subjects taken in AY2023 | Subjects deemed to have been taken in AY2024 |
|---|---|
| Applied Artificial Intelligence and Data Science A (XCO.T483) | Applied Practical Data Science and Artificial Intelligence 3A (DSA.P431) |
| Applied Artificial Intelligence and Data Science B (XCO.T484-01) | Applied Practical Data Science and Artificial Intelligence 2A (DSA.P421) |
| Applied Artificial Intelligence and Data Science C1 (XCO.T485-01) | Applied Practical Data Science and Artificial Intelligence 1A (DSA.P411) |
| Applied Artificial Intelligence and Data Science C2 (XCO.T485-02) | Applied Practical Data Science and Artificial Intelligence 1B(DSA.P412) |
| Applied Artificial Intelligence and Data Science D (XCO.T486) | Applied Practical Data Science and Artificial Intelligence 2B(DSA.P422) |
| Progressive Applied Artificial Intelligence and Data Science A (XCO.T687) | Progressive Applied Practical Data Science and Artificial Intelligence 3A (DSA.P631) |
| Progressive Applied Artificial Intelligence and Data Science B (XCO.T688) | Progressive Applied Practical Data Science and Artificial Intelligence 2A (DSA.P621) |
| Progressive Applied Artificial Intelligence and Data Science C1 (XCO.T689-01) | Progressive Applied Practical Data Science and Artificial Intelligence 1A(DSA.P611) |
| Progressive Applied Artificial Intelligence and Data Science C2 (XCO.T689-02) | Progressive Applied Practical Data Science and Artificial Intelligence 1B(DSA.P612) |
| Progressive Applied Artificial Intelligence and Data Science D (XCO.T690) | Progressive Applied Practical Data Science and Artificial Intelligence 2B(DSA.P622) |
TIMETABLE
2024-1Q
2024-2Q
| Mon | Tue | Wed | Thu | Fri | |
|---|---|---|---|---|---|
| 8:50 - 10:30 | |||||
| 10:45 - 12:25 | |||||
| 13:30 - 15:10 | Information Visualization (Ookayama) | Information Visualization (Ookayama) | |||
| 15:25 - 17:05 | Multimedia Information Processing (Ookayama) | Applied Practical Data Science and Artificial Intelligence 2A (Ookayama, Suzukakedai) Progressive Applied Practical Data Science and Artificial Intelligence 2A (Ookayama, Suzukakedai) | Applied Practical Data Science and Artificial Intelligence 2B (Ookayama, Suzukakedai) Progressive Applied Practical Data Science and Artificial Intelligence 2B (Ookayama, Suzukakedai) | Multimedia Information Processing (Ookayama) | Applied Practical Data Science and Artificial Intelligence 2C (Ookayama, Suzukakedai) Progressive Applied Practical Data Science and Artificial Intelligence 2C (Ookayama, Suzukakedai) |
| 17:15 - 18:55 |
【For reference】2023-3Q
【For reference】2023-4Q
| Mon | Tue | Wed | Thu | Fri | |
|---|---|---|---|---|---|
| 8:50 - 10:30 | |||||
| 10:45 - 12:25 | Complex Networks | Information Visualization | Complex Networks | Information Visualization | |
| 13:30 - 15:10 | Mathematical Models and Computer Science Fundamentals of Data Science (other schools, Japanese) Fundamentals of Progressive Data Science (other schools, Japanese) | Mathematical Models and Computer Science Fundamentals of Artificial Intelligence (other schools, Japanese) Fundamentals of Progressive Artificial Intelligence (other schools, Japanese) | |||
| 15:25 - 17:05 | Computer Graphics | Exercises in Fundamentals of Data Science (other schools, Japanese) Exercises in Fundamentals of Progressive Data Science (other schools, Japanese) | Computer Graphics | Exercises in Fundamentals of Artificial Intelligence (other schools, Japanese) Exercises in Fundamentals of Progressive Artificial Intelligence (other schools, Japanese) | |
| 17:15 - 18:55 |
GUIDE
Please read List of Study Guides and Student Handbooks.
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