2022 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 / Motoya Machida
- Class Format
- Lecture
- Media-enhanced courses
- -
- Day of week/Period
(Classrooms) - Intensive
- Class
- -
- Course Code
- ART.T454
- Number of credits
- 200
- Course offered
- 2022
- 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 | 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 |
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
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.