2020 Faculty Courses School of Computing Department of Computer Science Graduate major in Artificial Intelligence
Advanced Topics in Artificial Intelligence S
- Academic unit or major
- Graduate major in Artificial Intelligence
- Instructor(s)
- Toyotaro Suzumura
- Class Format
- Lecture (Zoom)
- Media-enhanced courses
- -
- Day of week/Period
(Classrooms) - Intensive
- Class
- -
- Course Code
- ART.T454
- Number of credits
- 200
- Course offered
- 2020
- Offered quarter
- 1-2Q
- Syllabus updated
- Jul 10, 2025
- Language
- English
Syllabus
Course overview and goals
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.
Course description and aims
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
- 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/Objectives
| Course schedule | Objectives | |
|---|---|---|
| Class 1 | Advanced topics on graph algorithms | Graph theory |
| Class 2 | Coding exercise | |
| Class 3 | Advanced topics on graph database | Graph theory |
| Class 4 | Coding exercise | |
| Class 5 | Advanced topics on graph learning | Machine learning |
| Class 6 | Coding exercise | |
| Class 7 | Advanced topics on graph neural network (I) | Neural network |
| Class 8 | Coding exercise | |
| Class 9 | Advanced topics on graph neural network (II) | Neural network |
| Class 10 | Coding exercise | |
| Class 11 | Advanced topics on high performance computing and graph learning for masssive graphs | High performance computing |
| Class 12 | Coding exercise | |
| Class 13 | Advanced topics on graph learning and use cases | Graph theory |
| Class 14 | Coding exercise |
Study advice (preparation and review)
Textbook(s)
None
Reference books, course materials, etc.
Specified by lecturers
Evaluation methods and criteria
Will be based on exercise and report.
Related courses
- None
Prerequisites
None
Other
The details will be announced later.