2024 Faculty Courses School of Environment and Society Department of Technology and Innovation Management Graduate major in Technology and Innovation Management
Advanced Methodology of Mathematical and Computational Analysi II
- Academic unit or major
- Graduate major in Technology and Innovation Management
- Instructor(s)
- Shintaro Ikeda
- Class Format
- Lecture/Exercise (Livestream)
- Media-enhanced courses
- -
- Day of week/Period
(Classrooms) - 11-12 Tue
- Class
- -
- Course Code
- TIM.A539
- Number of credits
- 0.50.50
- Course offered
- 2024
- Offered quarter
- 4Q
- Syllabus updated
- Mar 14, 2025
- Language
- Japanese
Syllabus
Course overview and goals
Deep learning and mathematical optimization methods are becoming indispensable technologies for industry and society. These technologies are not limited to specific fields, but have general-purpose characteristics, and are expected to be applied to a wide range of fields in the future.
In this lecture, we will first understand reinforcement learning and deep reinforcement learning. At the same time, we will understand optimization methods such as mathematical programming and meta-heuristics. Finally, we will understand the characteristics of quantum computing and its technological development trends.
In this course, even beginners of machine learning and mathematical programming will be able to learn easily, and learn the principles of algorithms and the ability to implement simple methods based on the application examples of each method.
Course description and aims
By taking this lecture, students will be able to understand and acquire the followings:
(1) Understand the development history and application scope of deep reinforcement learning.
(2) To understand the basics of mathematical programming and metaheuristics.
(3) To understand the outline of current quantum computing technology.
Student learning outcomes
実務経験と講義内容との関連 (又は実践的教育内容)
The teacher in charge was conducting AI and software development at a company he started by himself. This lecture is based on the knowledge gained through practical software development.
Keywords
Deep learning, Python, programming, reinforcement learning, mathematical programming, quantum computing
Competencies
- Specialist skills
- Intercultural skills
- Communication skills
- Critical thinking skills
- Practical and/or problem-solving skills
Class flow
The class will consist mainly of lectures, with some Python programming practice, using the browser-based development environment (Google Colab).
Course schedule/Objectives
| Course schedule | Objectives | |
|---|---|---|
| Class 1 | Guidance, Deep Reinforcement Learning (1) | To understand the purpose of this lecture. To understand the principles of reinforcement learning and the latest trends in value-based and strategy-based algorithms. |
| Class 2 | Deep Reinforcement Learning (2) (Programming practice) | Understand the principles of game reinforcement learning through examples. Implement deep reinforcement learning in Python as an example of reinforcement learning. |
| Class 3 | Overview of optimization and linear programming (1) | Understand examples of optimization and search algorithms and the concept of computational complexity classes. |
| Class 4 | Linear programming (2) (programming practice) | Understand the basic principles of linear programming and perform linear programming in Python. |
| Class 5 | Nonlinear Programming and Dynamic Programming | To understand nonlinear programming algorithms and dynamic programming and Dijkstra methods applied to optimal path problems. |
| Class 6 | Metaheuristics | Understand meta-heuristics methods such as genetic algorithms and swarm intelligence optimization. |
| Class 7 | Quantum computing | To understand the outline of quantum computing technology (quantum annealing method, quantum gate method, etc.). |
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)
Nothing
Reference books, course materials, etc.
Lecture materials prepared by the instructor will be used in the class.
Evaluation methods and criteria
Evaluation will be based on participation in lectures and programming practice (50%) and submission of reports (50%).
Related courses
- TIM.A405 ： Methodology of Mathematical and Computational Analysis I
- TIM.A406 ： Methodology of Mathematical and Computational Analysis II
- TIM.A538 ： Advanced Methodology of Mathematical and Computational Analysis I
Prerequisites
Nothing