2025 (Current Year) Faculty Courses School of Computing Department of Mathematical and Computing Science Graduate major in Mathematical and Computing Science

Topics on Mathematical and Computing Science OB

Academic unit or major: Graduate major in Mathematical and Computing Science
Instructor(s): Kenichi Kobayashi / Satoshi Munakata / Kenichirou Narita / Koichi Shirahata / Yusuke Nagasaka / Akira Sakai / Kanata Suzuki / Yuma Ichikawa / Hidehiko Masuhara
Class Format: Lecture (Face-to-face)
Media-enhanced courses: -
Day of week/Period (Classrooms): Intensive
Class: -
Course Code: MCS.T425
Number of credits: 200
Course offered: 2025
Offered quarter: 3-4Q
Syllabus updated: Dec 4, 2025
Language: Japanese

Syllabus

Course overview and goals

This class lectures recent topics related to mathematics and computer science. Recent research and development of each topic is introduced from the foundational theories and technologies to the advanced matters by the experts on the field. The topics covered in this year include artificial intelligence (AI), in particular large language models (LLMs) and machine learning exercised in an industrial research laboratory. By introducing mathematical research problems and implementation issues at the application of AI to real-world problems, participants will learn technology trends of the computing platform development as well as today's AI research.

Course description and aims

By the end of this course, participants deepen their overview of the recent research and development on the topics introduced in this course.

Student learning outcomes

実務経験と講義内容との関連 (又は実践的教育内容)

The course discussion is based on the experiences on the research and development in an industrial cooperation.

Keywords

Large language models, hallucination, generative AI, knowledge graph, simulation, robotics, machine learning, deep learning, mathematical optimizations

Competencies

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

Class flow

The classes will be conducted by presenting the main contents of the class and open for questions from the students at any time.

Course schedule/Objectives

	Course schedule	Objectives
Class 1	Large Language Models	understand an overview of LLMs
Class 2	Anti Hallucination Technologies	understand technical challenges in AI hallucinations
Class 3	Computing Technology for Accelerating AI and Simulation	understand the technological focus on today's computing infrastructure
Class 4	Massively Parallel Deep Learning by Using Supercomputers	understand technical challenges in applying supercomputers to deep learning
Class 5	Machine Learning for Robots Based on Programming by Demonstration	understand programming by demonstration and its application to robotics
Class 6	Robot Foundation Model for Robotics	understand the robot foundation models and their applications to robotics
Class 7	Image Processing AI for Ultrasonic Medical Images	understand an overview of image processing AI for ultrasonic medical images
Class 8	Image Processing AI for Frozen Tuna Inspection	understand an overview of image processing AI for ultrasonic frozen tuna inspection
Class 9	Mathematical Optimizations and Deep Learning	understand the relationships between mathematical optimizations and deep learning
Class 10	Compression of LLMs and its Mathematical Background	understand compression of LLMs from the viewpoint of mathematics
Class 11	.	.
Class 12	.	.
Class 13	.	.
Class 14	.	.

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)

Textbooks will not be used in this course.

Reference books, course materials, etc.

References will be announced in classes.

Evaluation methods and criteria

Explained at the first lecture

Related courses

Prerequisites

None.