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School of Engineering
- Undergraduate major in Mechanical Engineering
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- 工学院，物質理工学院，環境・社会理工学院共通科目
- Liberal arts and basic science courses
- First-Year Courses
- School of Science
- School of Engineering
- School of Materials and Chemical Technology
- School of Computing
- School of Life Science and Technology
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School of Environment and Society
- Department of Architecture and Building Engineering
- Department of Civil and Environmental Engineering
- Department of Transdisciplinary Science and Engineering
- Department of Social and Human Sciences
- Department of Innovation Science
- Graduate major in Technology and Innovation Management
- Common courses
- Liberal arts and basic science courses
- Academic unit or major
- Graduate major in Artificial Intelligence
- Instructor(s)
- Suzumura Toyotaro Machida Motoya
- Class Format
- Lecture
- Media-enhanced courses
- Day/Period(Room No.)
- Intensive ()
- Group
- -
- Course number
- ART.T454
- Credits
- 2
- Academic year
- 2022
- Offered quarter
- 1-2Q
- Syllabus updated
- 2022/3/16
- Lecture notes updated
- -
- Language used
- English
- Access Index
-
Course description and aims
In this intensive course, advanced topics in the wide range of informatics such as mathematical information sciences, intelligence sciences, life-sciences and socio-economic sciences are introduced by visiting lecturers.
The aim of this course is to broaden students' perspectives by lectures of advanced topics by active scientists in the front line.
Student learning outcomes
Students can obtain knowledge about advanced topics in mathematical information sciences, intelligence sciences, life sciences and socio-economic sciences.
Keywords
mathematical information sciences, intelligence sciences, life sciences, socio-economic sciences
Competencies that will be developed
| ✔ Specialist skills | Intercultural skills | Communication skills | ✔ Critical thinking skills | Practical and/or problem-solving skills |
Class flow
Lectures give intensive lectures about selected advanced topics.
Course schedule/Required learning
| Course schedule | Required learning | |
|---|---|---|
| Class 1 | Graph structures and graph analytics for real-world applications | Graph theory |
| Class 2 | Methods to understand graph structures | Graph theory |
| Class 3 | Introduction to Graph Neural Networks | Deep Learning |
| Class 4 | Graph Neural Networks for Heterogenous Graphs | Deep Learning |
| Class 5 | Graph Neural Networks for Time-Evolving Graphs | Deep Learning |
| Class 6 | Graph Neural Networks for Recommender Systems | Deep Learning |
| Class 7 | State-of-the-art research themes on Graph Neural Networks | Deep Learning |
| Class 8 | Motivation for Monte Carlo simulation / Rejection algorithm | Bayesian statistics and simulation |
| Class 9 | Markov chain Monte Carlo method / Metropolis algorithm | Markov chains and sampling algorithm |
| Class 10 | Discrete structure and Gibbs sampler / Gibbs algorithm | Gibbs model and sampling algorithm |
| Class 11 | How long should you run it? / Perfect sampling algorithms | Coupling and perfect sampling methods |
| Class 12 | Hidden Markov model and dynamic decision making / Viterbi algorithm | Hidden Markov model |
| Class 13 | Quantum computation and sampling / Shor’s algorithm | Quantum states and sampling |
| Class 14 | Brownian motion and intertwining dual / Pitman-type algorithm | Brownian motion and sampling algorithm |
Textbook(s)
None
Reference books, course materials, etc.
Specified by lecturers
Assessment criteria and methods
Will be based on exercise and report.
Related courses
- None
Prerequisites (i.e., required knowledge, skills, courses, etc.)
None
Contact information (e-mail and phone) Notice : Please replace from "[at]" to "@"(half-width character).
Toshiaki MUROFUSHI (murofusi[at]c.titech.ac.jp)
Other
The details will be announced later.
