2025 (Current Year) Faculty Courses School of Engineering Department of Industrial Engineering and Economics Graduate major in Industrial Engineering and Economics
Intelligent Systems Engineering
- Academic unit or major
- Graduate major in Industrial Engineering and Economics
- Instructor(s)
- Ryutaro Ichise
- Class Format
- Lecture (Face-to-face)
- Media-enhanced courses
- -
- Day of week/Period
(Classrooms) - 3-4 Tue (W9-508) / 3-4 Fri (W9-508)
- Class
- -
- Course Code
- IEE.C434
- Number of credits
- 200
- Course offered
- 2025
- Offered quarter
- 2Q
- Syllabus updated
- Jun 9, 2025
- Language
- English
Syllabus
Course overview and goals
An intelligent system is a system that carries out human intellectual tasks such as data analysis and decision-making on our behalf. In this lecture, we will discuss the elemental technologies required for designing intelligent systems. Furthermore, through the implementation of these elemental technologies, we will aim to understand the design methodology of intelligent systems.
Course description and aims
By taking this course, students will be able to acquire the following skills.
(1) Acquire basic theories and knowledge related to intelligent systems.
(2) Understand the main technologies of intelligent systems and be able to apply them to real-world problems.
(3) Acquire the ability to design, construct, and utilize intelligent systems in solving engineering problems.
Keywords
Artificial intelligence, machine learning, natural language processing, knowledge graphs
Competencies
- Specialist skills
- Intercultural skills
- Communication skills
- Critical thinking skills
- Practical and/or problem-solving skills
Class flow
The course will consist of lectures and related exercises. Based on the lectures, students will design and implement intelligent systems and present their results.
Course schedule/Objectives
| Course schedule | Objectives | |
|---|---|---|
| Class 1 | What is an intelligent system? |
Understanding the outline of intelligent systems |
| Class 2 | Intelligent Agent |
Understanding how human intelligence can be realized in machines |
| Class 3 | Intelligent System Design |
Understanding of methodology for designing intelligent systems |
| Class 4 | Machine Learning (1) |
Understanding the basics of machine learning and deep learning |
| Class 5 | Machine Learning Exercises (1) |
Understanding how to implement machine learning and deep learning |
| Class 6 | Machine Learning (2) |
Understanding the basics of reinforcement learning |
| Class 7 | Machine Learning Exercises (2) |
Understanding how to implement reinforcement learning |
| Class 8 | Natural Language Processing |
Understanding the basics of natural language processing |
| Class 9 | Natural Language Processing Exercises |
Understanding how to implement natural language processing |
| Class 10 | Intelligent Systems Applications |
Understanding how to apply intelligent systems to real-world problems |
| Class 11 | Exercise in Applied Intelligent Systems |
Understanding how to implement intelligent systems in real-world problems |
| Class 12 | Knowledge Graphs |
Understanding the basics of knowledge graphs |
| Class 13 | Knowledge Graph Exercises |
Understanding how to use knowledge graphs |
| Class 14 | Conclusion |
Understanding the issues of intelligent systems |
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)
Stuart Russell, Peter Norvig 『Artificial Intelligence: A Modern Approach』 Pearson
Reference books, course materials, etc.
Kazunori Matsumoto, Tetsuhiro Miyahara, Yasuo Nagai, Ryutaro Ichise: Artificial Intelligence, Ohm Sha (in Japanese)
Provide materials when needed.
Evaluation methods and criteria
Exercise, presentations and reports.
Related courses
- IEE.A204 ： Probability for Industrial Engineering and Economics
- IEE.A205 ： Statistics for Industrial Engineering and Economics
- IEE.A207 ： Computer Programming (Industrial Engineering and Economics)
Prerequisites
Students must have knowledge of how to use a computer and basic programming skills.
Students must be able to bring your own PC to class.