2019 Advanced Artificial Intelligence and Data Science C

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Academic unit or major
School of Computing
Instructor(s)
Miyake Yoshihiro  Shudo Kazuyuki  Kise Kenji  Saito Suguru  Nitta Katsumi  Hayashi Kidai  Fujimoto Shotaro  Yoshimoto Seiya  Tamura Tetsuya  Nakatsugawa Eiji  Iijima Akira  Kazawa Hideto 
Course component(s)
Lecture
Day/Period(Room No.)
Tue9-10(W531(レクチャーシアター),G115)  
Group
-
Course number
XCO.T485
Credits
1
Academic year
2019
Offered quarter
4Q
Syllabus updated
2019/12/2
Lecture notes updated
-
Language used
Japanese
Access Index

Course description and aims

This course is designed for students to understand the outline of digital art, automobiles, machine translation, and online advertising to consider the possibility to utilize artificial intelligence and data science in the field.
The lecturers will explain broad pictures and recent trends of the topic in each class, as shown below.

Student learning outcomes

This course aims to develop ability of each student to be more successful in the real world with the consideration of artificial intelligence and data science, and also through the opportunity for students to describe their own ideas.

Keywords

artificial intelligence, data science, connected cars, autonomous driving, machine translation, online advertising

Competencies that will be developed

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

Class flow

This course requires students to take an active role in their own learning. It is required to attend each class.

Course schedule/Required learning

  Course schedule Required learning
Class 1 Introduction to artificial intelligence and data utilization through examples To understand the possibilities of artificial intelligence and data utilization on actual solution examples.
Class 2 Usage of artificial intelligence for digital art (1) To understand the outline and mechanism of art works using artificial intelligence.
Class 3 Usage of artificial intelligence for digital art (2) To understand the algorithm selection and design process assuming virtual works.
Class 4 Usage of artificial intelligence for digital art (3) To understand techniques for creating interactions using deep learning.
Class 5 Technology development and prospects related to artificial intelligence / big data required for the automobile industry To understand artificial intelligence / big data technology and future prospects necessary to achieve the needs of automobiles required by modern society.
Class 6 The future of connected cars and autonomous driving To focus on connected and autonomous driving in automotive trends and understand the services that spread from the car which can connect with society and the technology required for autonomous driving.
Class 7 Usage of machine learning in machine translation To understand the basics of natural language processing using deep learning and its application to machine translation.
Class 8 Applications of machine learning and data science in online advertising To understand the use cases of machine learning and data science in online advertising.

Textbook(s)

None required

Reference books, course materials, etc.

Materials will be provided on OCW-i in advance and projected in the class room

Assessment criteria and methods

Based on quizzes evaluating students' understanding at the end of each class.

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.T486 : Advanced Artificial Intelligence and Data Science D

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

none

Other

This lecture is supported by teamLab Inc., Toyota Motor Corporation, and Google Japan G.K.

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