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School of Engineering
- Undergraduate major in Mechanical Engineering
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- School of Materials and Chemical Technology
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- 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
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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
- Graduate major in Industrial Engineering and Economics
- Instructor(s)
- Nakata Kazuhide
- Class Format
- Lecture
- Media-enhanced courses
- Day/Period(Room No.)
- -
- Group
- -
- Course number
- IEE.A430
- Credits
- 2
- Academic year
- 2024
- Offered quarter
- 4Q
- Syllabus updated
- 2024/3/14
- Lecture notes updated
- -
- Language used
- Japanese
- Access Index
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Course description and aims
In this lecture, students will learn about mathematical theory and other topics related to machine learning.
Student learning outcomes
By the end of this course, students will be able to:
1. Understand the theoretical properties of machine learning and can apply them to real problems.
Keywords
Optimization, Machine learning
Competencies that will be developed
| ✔ 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/Required learning
| Course schedule | Required learning | |
|---|---|---|
| Class 1 | nonlinear optimization 1 | We instruct in each class |
| Class 2 | nonlinear optimization 2 | We instruct in each class |
| Class 3 | nonlinear optimization 3 | We instruct in each class |
| Class 4 | feature extraction 1 | We instruct in each class |
| Class 5 | feature extraction 2 | We instruct in each class |
| Class 6 | feature extraction 3 | We instruct in each class |
| Class 7 | mid-term test | We instruct in each class |
| Class 8 | clustering 1 | We instruct in each class |
| Class 9 | clustering 2 | We instruct in each class |
| Class 10 | clustering 3 | We instruct in each class |
| Class 11 | generative model 1 | We instruct in each class |
| Class 12 | generative model 2 | We instruct in each class |
| Class 13 | generative model 3 | We instruct in each class |
| Class 14 | mid-term test | We instruct in each class |
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 required
Reference books, course materials, etc.
Course materials can be found on T2SCHOLA
Assessment criteria and methods
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 (i.e., required knowledge, skills, courses, etc.)
No prerequisites
