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- School of Science
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School of Engineering
- Undergraduate major in Mechanical Engineering
- Undergraduate major in Systems and Control Engineering
- Undergraduate major in Electrical and Electronic Engineering
- Undergraduate major in Information and Communications Engineering
- Undergraduate major in Industrial Engineering and Economics
- Common courses
- School of Materials and Chemical Technology
- School of Computing
- School of Life Science and Technology
- School of Environment and Society
- 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
-
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 Alexander Shibakov
- Class Format
- Lecture
- Media-enhanced courses
- Day/Period(Room No.)
- Intensive ()
- Group
- -
- Course number
- ART.T454
- Credits
- 2
- Academic year
- 2018
- Offered quarter
- 1-2Q
- Syllabus updated
- 2018/7/27
- Lecture notes updated
- 2018/6/29
- 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 instructors.
The aim of this course is to broaden students' perspectives through lectures of advanced topics by active front line scientists.
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
Instructors give lectures about selected advanced topics.
Course schedule/Required learning
| Course schedule | Required learning | |
|---|---|---|
| Class 1 | Various distributions and related stochastic models. | Study of advanced topics |
| Class 2 | Poisson processes and their properties. | Study of advanced topics |
| Class 3 | Filtration and martingale in stochastic processes. | Study of advanced topics |
| Class 4 | Counting processes, hazard functions, and martingale transform. | Study of advanced topics |
| Class 5 | Random censorship model, survival function, and Kaplan-Meier estimator. | Study of advanced topics |
| Class 6 | Expectation, variance, and asymptotic properties of martingale transform. | Study of advanced topics |
| Class 7 | Comparison of two groups in survival analysis: Linear rank statistics. | Study of advanced topics |
| Class 8 | Introduction to large-scale graph analytics | Study of advanced topics |
| Class 9 | Large-scale graph database | Study of advanced topics |
| Class 10 | Large-scale graph analytics and high-performance computing | Study of advanced topics |
| Class 11 | Integration of large-scale graph analytics and machine learning | Study of advanced topics |
| Class 12 | Applications of graph-based machine learning | Study of advanced topics |
| Class 13 | Deep learning-based graph embedding method | Study of advanced topics |
| Class 14 | Introduction to knowledge graph | Study of advanced topics |
| Class 15 | Knowledge graph and its application to articial intelligence | Study of advanced topics |
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
Other
The details will be announced later.
