2021 Advanced Artificial Intelligence and Data Science C

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Academic unit or major
School of Computing
Instructor(s)
Kanezaki Asako  Miyake Yoshihiro  Nitta Katsumi  Nagahashi Hiroshi  Kobayashi Takao  Tsurumi Toshiyuki  Sato Akiko  Tamura Tetsuya  Yamada Takeshi  Fujimoto Shotaro  Hayashi Kidai  Yoshimoto Seiya  Nishida Daishiro  Nakano Yuya  Tomita Hayato 
Course component(s)
Lecture    (ZOOM)
Day/Period(Room No.)
Tue9-10()  
Group
-
Course number
XCO.T485
Credits
1
Academic year
2021
Offered quarter
1Q
Syllabus updated
2021/3/19
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.

Course taught by instructors with work experience

Applicable How instructors' work experience benefits the course
This lectured are given by scientists and engineers of Nefrock Inc. and Team Lab Inc. about application of AI and Data Science to solve practical problems.

Keywords

artificial intelligence, data science, digital art

Competencies that will be developed

Specialist skills Intercultural skills Communication 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 AI development from planning through case studies To understand the viewpoints necessary for AI development through a series of examples from planning to user use.
Class 2 Planning Approach for AI Development (1) To understand the viewpoints necessary for AI development through a user-driven approach technology required for autonomous driving.
Class 3 Planning Approach for AI Development (2) To understand the viewpoints necessary for AI development through a user-driven approach .
Class 4 Introduction to artificial intelligence and data utilization through examples To understand the possibility of utilization of AI and data based on actual cases.
Class 5 Usage of artificial intelligence for digital art (1) To understand the overview of work of digital art
Class 6 Usage of artificial intelligence for digital art (2) To understand the algorithm selection and design process for virtual digital arts.
Class 7 Usage of artificial intelligence for digital art (3) To understand the production process of interaction mechanism using the deep learning

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 course material.

Textbook(s)

None required

Reference books, course materials, etc.

Materials will be provided on OCW-i in advance and shared in Zoom lecture

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 Nefrock Inc. and TeamLab Inc..

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