Science of complex systems-Data Science2-
| Numbering Code |
G-GAIS00 54006 LB13 G-GAIS00 54006 LB54 |
Year/Term | 2022 ・ Second semester | |
|---|---|---|---|---|
| Number of Credits | 2 | Course Type | Lecture | |
| Target Year | From 1st to 3rd year students | Target Student | ||
| Language | Japanese and English | Day/Period | Tue.2 | |
| Instructor name | IKEDA YUICHI (Graduate School of Advanced Integrated Studies in Human Survivability Professor) | |||
| Outline and Purpose of the Course |
Modeling of complex phenomena is essential to elucidate global problems involving a variety of economic and social factors. Through the co-use of data analysis and simulation, students will understand systems in which multiple components manifest holistic properties that individual components do not possess through strong interactions. To understand the basics of data science, network science, and computational science, and to learn their specific applications. This course is designed to provide students with an understanding of the fundamentals of data science, network science, and computational science and to study specific applications of those fundamentals. This course's objective is to learn modeling and simulation, which are the basics of data science. At the same time, we will try to improve our English language skills in this field through English and Japanese lectures. |
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| Course Goals | Students will have an understanding of the basic concepts of network science and complex systems and be able to model and simulate the phenomena of interest to each student using Python. | |||
| Schedule and Contents |
【1st】Introduction: Network Science and Machine Learning 【2nd】Machine Learning 1: Unsupervised Learning (Principal Component Analysis) 【3rd】Machine Learning 2: Supervised Learning (Lasso and Ridge Regression) 【4th】Machine Learning 3: Supervised/Unsupervised Learning (K-Nearest Neighbor Classifier and Cluster Analysis) 【5th】Discussion 1: Problem Setting 【6th】Network Structure 1: Centralities 【7th】Network Structure 2: Network Generation Model 【8th】Network Structure 3: Community Analysis 【9th】Network Dynamics 1: System Dynamics 【10th】Discussion 2: Modeling and Programing 【11th】Network Dynamics 2: Network Epidemiology 【12th】Network Dynamics 3: Collective Motion (Kuramoto Model and Ising Model) 【13th】Machine Learning 4: Unsupervised Learning (Natural Language Processing) 【14th】Graph Neural Network 【15th】Discussion 3: Results and Discussion |
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| Evaluation Methods and Policy | Evaluation is made based on behavior in class and the final report. | |||
| Course Requirements | None | |||
| Study outside of Class (preparation and review) | Preparation of discussion has to be made as assignment. | |||
| Textbooks | Textbooks/References | Printed materials will be distributed in class. | ||
| References, etc. | References will be shown according to needs. | |||
