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- 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
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School of Environment and Society
- Department of Architecture and Building Engineering
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- 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 (HyFlex)
- Media-enhanced courses
- Day/Period(Room No.)
- Intensive ()
- Group
- -
- Course number
- ART.T454
- Credits
- 2
- Academic year
- 2024
- Offered quarter
- 1-2Q
- Syllabus updated
- 2024/3/14
- 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.
advanced topics in 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' perspective by lectures of advanced topics by active scientists in the front line.
In the first 7 lectures, a wide variety of things and their relationships in the digital and physical world can be represented as graph. In the first 7 lectures, we study deep learning based methods called "graph neural networks or GNNs" that enable representation learning on graph-structured data. We also learn how GNNs can be used for real-world applications such as recommender systems, anomaly detection in financial institutions, material discovery, and so forth.
In the second half of lecture series, we present Markov chain Monte Carlo (MCMC) methods and closely related stochastic algorithms. We begin our discussion with the review of Markov chains and random algorithms in a general setting, preparing the stage for the study of various implementations of stochastic algorithms. We explore other interesting topics such as hidden Markov models, Langevin algorithms, and applications in Bayesian statistics.
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 | Review for probabilistic approach and Monte Carlo 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 | Brownian motion and Monte Carlo simulation / Langevin algorithm | Brownian motion and sampling algorithm |
| Class 14 | MCMC in practice / Bayesian computation | Bayesian methods and simulation |
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).
MUROFUSHI, Toshiaki (murofusi[at]c.titech.ac.jp)
Other
The details will be announced later.
