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2022　Practical Artificial Intelligence and Data Science B 1

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Academic unit or major
School of Computing
Instructor(s)
Miyake Yoshihiro  Murata Tsuyoshi  Nitta Katsumi  Nagahashi Hiroshi  Kobayashi Takao  Sasao Kazuhiro  Nishimoto Hideaki  Inokuchi Ryo  Nomura Takehiko  Terada Akihiro  Morita Katsuaki 
Class Format
Lecture     
Media-enhanced courses
Day/Period(Room No.)
Fri7-8()  
Group
1
Course number
XCO.T494
Credits
1
Academic year
2022
Offered quarter
2Q
Syllabus updated
2022/5/9
Lecture notes updated
-
Language used
Japanese
Access Index

Course description and aims

The purpose of this course is to understand the current status of social implementation of AI and data science technologies and cutting-edge technologies, and to examine the applicability and challenges of these technologies. Trends and issues in technology and product development in the fields of fintech, IT telecommunication, manufacturing, heavy industry, construction etc. will be explained in each class as shown in the course schedule.

Student learning outcomes

The goal of this course is for students to acquire knowledge of AI and data science technologies in various fields, and to gain a broader perspective that will enable them to play an active role in the real world by discussing social applications and explaining new ideas in assignment reports.

Course taught by instructors with work experience

✔ Applicable How instructors' work experience benefits the course
In this course, lectures based on practical experience are given by lecturers of 　(class 1) NS Solutions Inc., Dai-ichi Life Group Inc., Mizuho Financial Group Inc., Furukawa Electric Inc., Fanuc Co., Mitsubishi Heavy Industries Inc., (class 2) Hitachi Ltd., Kajima Co., Mitsui Chemicals Inc., NTT East Co., and SUBARU Co.

Keywords

Data Science, Artificial Intelligence, Life Insurance, Fintec, Material, Manufacturing Industry, Heavy Industry, Construction

Competencies that will be developed

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

Class flow

Class1－Class7： Lectures

Course schedule/Required learning

  Course schedule Required learning
Class 1 Introduction to Ambient Computing - Development of Ambient Computing This lecture will outline the development of XR/IoT/AI and other component technologies of ambient computing, which is a technology for use in an era when computers are integrated into all environments.
Class 2 アンビニエントコンピューティング概論－アンビエントコンピューティングと Introduction to Ambient Computing - Ambient Computing and People Understanding human behavior is important for ambient computing. This lecture will provide an overview of techniques for understanding human behavior and explain various issues, including the ethical aspects of using human subjects.
Class 3 The Use of Data Science in Life Insurance Companies The role of data science in the life insurance business and its applications will be introduced, using the analysis of medical data in the development of life insurance products as a case study.
Class 4 Financial Data Analytics in Practice Machine learning and statistical science are increasingly being used in banks and other financial institutions. This lecture will explain the characteristics and approaches of data analytics in the financial domain. Challenges and issues to be addressed in the future will also be explained, focusing on the technical aspects.
Class 5 AI Application Examples in Manufacturing / Kotozukuri （Creating things) Introduce examples of AI applications in manufacturing and kotozukuri to understand the expectations and future vision of digital technology from the manufacturing industry's point of view.
Class 6 AI in manufacturing For students whose major is information, this lecture introduces AI in "things" that form the basis of daily life, AI in "manufacturing," the manufacturing industry that produces such things, and AI in business innovation from "things" to "koto(things).” Students will learn how to capture real information from manufacturing sites in a cyber-physical system, and how to utilize data and AI to enhance manufacturing competitiveness.
Class 7 AI utilization in energy solution services This lecture will provide an overview and specific examples of MHI's AI-based energy solution services, and will give examples of related AI technologies, including how to proceed with data analysis and machine learning methods.

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.

Textbook(s)

none

Reference books, course materials, etc.

Materials will be provided on T2SCHOLA in advance and shared in the Zoom lecture

Assessment criteria and methods

Mainly short report required in each class will be considered

Related courses

  • XCO.T487 ： Fundamentals of data science
  • XCO.T488 ： Exercises in fundamentals of data science
  • XCO.T489 ： Fundamentals of artificial intelligence
  • XCO.T490 ： Exercises in fundamentals of artificial intelligence
  • XCO.T483 ： Advanced Artificial Intelligence and Data Science A
  • XCO.T484 ： Advanced Artificial Intelligence and Data Science B
  • XCO.T485 ： Advanced Artificial Intelligence and Data Science C
  • XCO.T486 ： Advanced Artificial Intelligence and Data Science D

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

Both credits of Practical Artificial Intelligence and Data Science B-1 and B-2 cannot be obtained. Priority may be given to students enrolled in the Progressive Graduate Minor in Data Science and Artificial Intelligence.

Other

Slide distribution and report acceptance will be done by T2SCHOLA. For more information, please refer to the following site.
https://sites.google.com/view/tokyotechdsai/jissen

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