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- School of Science
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School of Engineering
- Undergraduate major in Mechanical Engineering
- Undergraduate major in Systems and Control Engineering
- Undergraduate major in Electrical and Electronic Engineering
- Undergraduate major in Information and Communications Engineering
- Undergraduate major in Industrial Engineering and Economics
- Common courses
- School of Materials and Chemical Technology
- School of Computing
- School of Life Science and Technology
- School of Environment and Society
- 工学院，物質理工学院，環境・社会理工学院共通科目
- Liberal arts and basic science courses
- First-Year Courses
- School of Science
- School of Engineering
- School of Materials and Chemical Technology
- School of Computing
- School of Life Science and Technology
-
School of Environment and Society
- Department of Architecture and Building Engineering
- Department of Civil and Environmental Engineering
- Department of Transdisciplinary Science and Engineering
- Department of Social and Human Sciences
- Department of Innovation Science
- Graduate major in Technology and Innovation Management
- Common courses
- Liberal arts and basic science courses
- Academic unit or major
- School of Computing
- Instructor(s)
- Miyoshi Naoto Miura Hideyuki Umehara Masaaki Watanabe Sumio
- Class Format
- Lecture
- Media-enhanced courses
- Day/Period(Room No.)
- Thr5-6(H101)
- Group
- -
- Course number
- XCO.B103
- Credits
- 1
- Academic year
- 2021
- Offered quarter
- 4Q
- Syllabus updated
- 2021/8/23
- Lecture notes updated
- -
- Language used
- Japanese
- Access Index
-
Course description and aims
Understand the basics of discrete structures, differential equations, machine learning, discrete probabilities, etc., which are part of the curriculum of the Department of Mathematical and Computational Science. In these fields, we construct mathematical models that extract only the essence of real problems, and develop mathematical and probability theories that are conscious of algorithms that implement this model on a computer. Among them, the explanation will focus on themes that do not use much specialized background knowledge.
Student learning outcomes
Understand the basics of discrete structures, differential equations, machine learning, discrete probabilities, etc., which are part of the curriculum of the Department of Mathematical and Computational Science.
Keywords
Discrete structures, differential equations, mathematical optimizations, discrete probability
Competencies that will be developed
| ✔ Specialist skills | Intercultural skills | Communication skills | Critical thinking skills | ✔ Practical and/or problem-solving skills |
Class flow
Four faculty members will give lectures on each topic in omnibus format.
Course schedule/Required learning
| Course schedule | Required learning | |
|---|---|---|
| Class 1 | Introduction to machine learning | Introduction to machine learning. |
| Class 2 | Discrete probability | To understand probability on discrete space and its applications. |
| Class 3 | Discrete random variables and expectations | To understand discrete-valued random variables and their applications. |
| Class 4 | Introduction to distributions 1 | To understand the definition of distributions introduced by L. Schwartz and its background. |
| Class 5 | Introduction to distributions 2 | To understand applications of distributions to differential equations and so on. |
| Class 6 | Topics on Plane Curves 1 | Fundamental properties of plane curves, length, curvature. |
| Class 7 | Topics on Plane Curves 2 | Rotation index, regular homotopy, etc |
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 textbooks and other course material.
Textbook(s)
None.
Reference books, course materials, etc.
None.
Assessment criteria and methods
Report
Related courses
- XCO.B101 ： Foundations of Computing 1
- XCO.B102 ： Foundations of Computing 2
Prerequisites (i.e., required knowledge, skills, courses, etc.)
No requirements.
