2025 (Current Year) Faculty Courses School of Engineering Department of Industrial Engineering and Economics Graduate major in Industrial Engineering and Economics
Numerical Optimization
- Academic unit or major
- Graduate major in Industrial Engineering and Economics
- Instructor(s)
- Kazuhide Nakata / Ken Kobayashi
- Class Format
- Lecture (Face-to-face)
- Media-enhanced courses
- -
- Day of week/Period
(Classrooms) - 3-4 Tue (W9-201(W921)) / 3-4 Fri (W9-203)
- Class
- -
- Course Code
- IEE.A430
- Number of credits
- 200
- Course offered
- 2025
- Offered quarter
- 4Q
- Syllabus updated
- Mar 19, 2025
- Language
- Japanese
Syllabus
Course overview and goals
In this lecture, students will learn about mathematical theory and other topics related to machine learning and mathematical optimization.
Course description and aims
By the end of this course, students will be able to:
1. Understand the theoretical properties of machine learning and mathematical optimization and apply them to real problems.
Keywords
Mathematical Optimization, Machine learning
Competencies
- Specialist skills
- Intercultural skills
- Communication skills
- Critical thinking skills
- Practical and/or problem-solving skills
Class flow
Attendance is taken in every class.
Students are required to read the text before coming to class.
Course schedule/Objectives
| Course schedule | Objectives | |
|---|---|---|
| Class 1 | supervised learning 1 |
We instruct in each class |
| Class 2 | supervised learning 2 |
We instruct in each class |
| Class 3 | support vector machine 1 |
We instruct in each class |
| Class 4 | support vector machine 2 |
We instruct in each class |
| Class 5 | ensemble learning 1 |
We instruct in each class |
| Class 6 | ensemble learning 2 |
We instruct in each class |
| Class 7 | neural network |
We instruct in each class |
| Class 8 | convex sets |
We instruct in each class |
| Class 9 | Lipschitz continuous differentiable functions |
We instruct in each class |
| Class 10 | optimality conditions |
We instruct in each class |
| Class 11 | unconstrained optimization problems |
We instruct in each class |
| Class 12 | steepest descent method |
We instruct in each class |
| Class 13 | Newton method |
We instruct in each class |
| Class 14 | conjugate gradient method and quasi-Newton method |
We instruct in each class |
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 required
Reference books, course materials, etc.
Course materials can be found on T2SCHOLA
Evaluation methods and criteria
Students will be assessed on their understanding of machine learning and text mining.
Students' course scores are based on tests and reports.
Related courses
- IEE.A206 ： Operations Research
- IEE.A330 ： Advanced Operations Research
- IEE.A331 ： OR and Modeling
Prerequisites
No prerequisites