Exercises in Fundamentals of Progressive Artificial Intelligence

Academic unit or major

Major courses

Instructor(s)

Kei Miyazaki / Keisuke Yanagisawa / Norio Tomii / Takayoshi Yokota / Naoaki Okazaki / Masamichi Shimosaka / Masakazu Sekijima / Keiji Okumura / Katsumi Nitta / Yoshihiro Miyake / Isao Ono

Class Format

Exercise (HyFlex)

Media-enhanced courses

-

Day of week/Period
(Classrooms)

7-8 Mon (S6-219, S6-211, S6-109, すずかけ台図書館 情報ネットワーク演習室)

Class

-

Course Code

XCO.T680

Number of credits

010

Course offered

2025

Offered quarter

3Q

Syllabus updated

Sep 10, 2025

Language

English

Syllabus

Course overview and goals

Artificial Intelligence is a research area that aims at artificially creating intelligence like humans. In recent years, artificial　intelligence was successfully applied to various domains with the advances on machine learning and deep learning utilizing big data and computation power. This lecture expects students to acquire skills that is essential for creating applications of artificial intelligence, implementing basic concepts and theories as a computer program.

Course description and aims

Students will be able to acquire skills that is essential for creating applications of artificial intelligence, experiencing data processing and machine learning on computers.

Keywords

classification, regression, gradient-based method, perceptron, activation function, backpropagation, automatic differentiation, convolutional neural network

Competencies

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

Class flow

In class, students are required to solve exercises that are linked with the contents of taught course "XCO. 679 Fundamentals of Progressive Artificial Intelligence".

Course schedule/Objectives

Study advice (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.

Course materials are distributed via Science Tokyo LMS.

Evaluation methods and criteria

Based on reports for given assignments.

Related courses

• XCO.T679 ： Fundamentals of Progressive Artificial Intelligence
• XCO.T687 ： Progressive Applied Artificial Intelligence and Data Science A
• XCO.T688 ： Progressive Applied Artificial Intelligence and Data Science B
• XCO.T689 ： Progressive Applied Artificial Intelligence and Data Science C
• XCO.T690 ： Progressive Applied Artificial Intelligence and Data Science D
• XCO.T677 ： Fundamentals of Progressive Data Science
• XCO.T678 ： Exercises in Fundamentals of Progressive Data Science

Prerequisites

This exercise is for the doctor course students. When you apply this exercise, it is strongly recommended to take "XCO.T679 Fundamentals of Progressive Artificial Intelligence", "XCO.T677 Fundamentals of Progressive Data Science" and "XCO.T678 Exercises in Fundamentals of Progressive Data Science" of the same quarter of the same year in parallel.

Contact information (e-mail and phone) Notice : Please replace from ”[at]” to ”@”(half-width character).

Questions should be sent to the following mailing list.
efai-2025-3q[at]dsai.isct.ac.jp

Other

Exercises are carried out using Google Colaboratory. Students are required to get Google accounts and to get ready for using
"fileupload/download" in Google Drive.
At the Suzukakedai Campus, the exercise room is located inside the library, so a student ID card is required for entry.
To use the PCs in the exercise room, you need to check your “Login ID” and set a “Password” through the Education Computer System on the Tokyo Tech Portal. Please make sure to complete this procedure before the exercise begins.