2023 Progressive Advanced Data Science and Artificial Intelligence 2

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Academic unit or major
Center of Data Science and Artificial Intelligence
Ishii Hideaki  Ichise Ryutaro  Manzhos Sergei  Ozaki Junichi  Nitta Katsumi  Ono Isao  Miyake Yoshihiro  Motomura Yoichi  Yamazaki Keisuke 
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Course description and aims

Today, utilization of computation and data is required in various fields. In this course, we teach methods for analyzing and utilizing data using computers, which are important to be active as researchers and engineers in science and engineering. The course covers advanced topics that are not covered in the courses of Fundamentals of Data Science and Fundamentals of Progressive Data Science.

Student learning outcomes

The goal is to understand how to use computers to analyze and utilize data.


Bayesian Network (Probabilistic Inference Model),Variational Bayesian Method,anomaly detection,anomaly detection,Simulation,Knowledge Graphs

Competencies that will be developed

Specialist skills Intercultural skills Communication skills Critical thinking skills Practical and/or problem-solving skills

Class flow

ZOOM is used to allow students to take courses at Ookayama or Suzukakedai campuses.

Course schedule/Required learning

  Course schedule Required learning
Class 1 Bayesian network (Probabilistic Inference Model) Understanding mechanisms for constructing Bayesian networks (probabilistic models) from big data and probabilistic inference algorithms for prediction and simulation of real-world phenomena.
Class 2 Variational Bayesian Method Understanding the variational Bayesian algorithm and its application to DNN.
Class 3 Time Series Analysis Understanding methods for analyzing changes and patterns in data over time.
Class 4 Anomaly Detection Understanding the methods used to automatically identify anomalous behavior in a dataset that deviates from normal behavior.
Class 5 Simulation and AI Understanding methods and examples of the fusion of simulation and AI.
Class 6 Knowledge Graphs Understanding of knowledge graphs and their applications.
Class 7 Application of Data Science and Artificial Intelligence Techniques to Frontier Research Understanding applications of machine learning and data-based techniques in physical sciences and renewable energy technologies, including materials informatics for the discovery of new functional materials, machine learning improvement of modeling methods, and ML-assisted renewable energy system management.

Out-of-Class Study Time (Preparation and Review)

To enhance effective learning, students are encouraged to spend approximately 100 minutes preparing for class and another 100 minutes reviewing class content afterwards (including assignments) for each class.
They should do so by referring to textbooks and other course material.



Reference books, course materials, etc.

Distributed electronically at T2SCHOLA.

Assessment criteria and methods

Evaluation is based on in-class assignments and reports, and advanced assignment reports.

Related courses

  • Fundamentals of progressive data science(XCO.T677)
  • Exercises in fundamentals of progressive data science(XCO.T678)
  • Fundamentals of progressive artificial intelligence(XCO.T679)
  • Exercises in fundamentals of progressive artificial intelligence(XCO.T680)

Prerequisites (i.e., required knowledge, skills, courses, etc.)

- Students should have basic knowledge of linear algebra, differential and integral calculus, and mathematical statistics.
- Students should be able to understand the content taught in Fundamentals of Data Science or Fundamentals of Progressive Data Science, as well as in Exercises in Fundamentals of Data Science or Exercises in Fundamentals of Progressive Data Science.

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