2026 (Current Year) Faculty Courses School of Computing Undergraduate major in Mathematical and Computing Science
Discrete Mathematics
- Academic unit or major
- Undergraduate major in Mathematical and Computing Science
- Instructor(s)
- Sakie Suzuki / Masaaki Umehara / Zin Arai / Shunsuke Tsuchioka / Shinya Nishibata / Jin Takahashi
- Class Format
- Lecture (Face-to-face)
- Media-enhanced courses
- -
- Day of week/Period
(Classrooms) - 7-8 Mon (W8E-308(W834)) / 7-8 Thu (W8E-308(W834))
- Class
- -
- Course Code
- MCS.T331
- Number of credits
- 200
- Course offered
- 2026
- Offered quarter
- 2Q
- Syllabus updated
- Apr 6, 2026
- Language
- Japanese
Syllabus
Course overview and goals
Discrete mathematics plays an important role in mathematical and computing sciences. The objective of this course is to provide the fundamentals of discrete mathematics.
Course description and aims
The students are expected to understand the fundamentals of discrete mathematics appeared in mathematical and computing sciences and also to be able to apply them to practical problems.
Keywords
Four color problem, Knot theory, Quantum topology, Experimental Mathematics, Ramanujan, Gauss, AI, Number theory
Competencies
- Specialist skills
- Intercultural skills
- Communication skills
- Critical thinking skills
- Practical and/or problem-solving skills
Class flow
The lectures provide the fundamentals of discrete mathematics.
Course schedule/Objectives
| Course schedule | Objectives | |
|---|---|---|
| Class 1 | Curvature and Euler characteristic |
Understand the contents covered by the lecture. |
| Class 2 | Four color problem I |
Understand the contents covered by the lecture. |
| Class 3 | Four color problem II |
Understand the contents covered by the lecture. |
| Class 4 | Four color problem IIII |
Understand the contents covered by the lecture. |
| Class 5 | Knots and links |
Understand the contents covered by the lecture. |
| Class 6 | Knot invariants |
Understand the contents covered by the lecture. |
| Class 7 | Jones polynomial and Quantum invariants |
Understand the contents covered by the lecture. |
| Class 8 | Experimental mathematics and Ramanujan |
Understand the contents covered by the lecture. |
| Class 9 | Introduction to machine learning (MNIST) |
Understand the contents covered by the lecture. |
| Class 10 | Introduction to machine learning (VAE) |
Understand the contents covered by the lecture. |
| Class 11 | Experimental mathematics and machine learning |
Understand the contents covered by the lecture. |
| Class 12 | Experimental mathematics and Gauss |
Understand the contents covered by the lecture. |
| Class 13 | Reciprocity law and Gauss sum |
Understand the contents covered by the lecture. |
| Class 14 | ENIAC and Gauss sum |
Understand the contents covered by the lecture. |
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)
Not specified.
Reference books, course materials, etc.
Not specified.
Evaluation methods and criteria
By scores of reports.
Related courses
- MCS.T231 ： Algebra
- MCS.T201 ： Set and Topology I
- MCS.T202 ： Exercises in Set and Topology I
Prerequisites
None.
Contact information (e-mail and phone) Notice : Please replace from ”[at]” to ”@”(half-width character).
Sakie Suzuki (sakie[at]comp.isct.ac.jp)
Office hours
To be announced in the first class of each instructor.