2020 Advanced Topics in Artificial Intelligence S

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Academic unit or major
Graduate major in Artificial Intelligence
Instructor(s)
Suzumura Toyotaro 
Course component(s)
Lecture
Day/Period(Room No.)
Intensive ()  
Group
-
Course number
ART.T454
Credits
2
Academic year
2020
Offered quarter
1-2Q
Syllabus updated
2020/4/28
Lecture notes updated
2020/5/14
Language used
English
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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

Intercultural skills Communication skills Specialist 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 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

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.

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