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 Analysis I
- 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.A538
- Number of credits
- 0.50.50
- Course offered
- 2024
- Offered quarter
- 3Q
- Syllabus updated
- Mar 14, 2025
- Language
- Japanese
Syllabus
Course overview and goals
Dramatic advances in deep learning technology have brought about major changes not only in the development of academic research, but also in industry and society. Image recognition AI and game AI have already been shown to surpass human capabilities, and new algorithms are constantly being proposed and put to practical use in classical optimization and search technologies. New algorithms are being proposed and put to practical use in classical optimization and search techniques. In addition, quantum computing is gradually being applied in limited situations (e.g., combinatorial optimization). In the future, basic knowledge of these advanced technologies and understanding of their technical limitations will be required not only for engineers in the field but also for management persons
Since this lecture is targeted at students studying technology management, we will avoid detailed theoretical explanations of mathematics as much as possible, and aim to acquire a wide range of basic knowledge related to deep learning. In addition, by creating simple models through programming practice, the objective is for the participants to correctly understand the merits and demerits of deep learning and the scope of its application.
The programming practice in this lecture is intended to be at a level corresponding to beginners of Python. This lecture is designed to be easy to learn even for beginners of machine learning, and the students will acquire 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 understand and acquire the following.
(1) Understand the development trend, various problems, and business cases of deep learning.
(2) To understand the outline of machine learning algorithms for image recognition and natural language processing.
(3) To correctly understand the advantages and disadvantages of deep learning, its application scope, and technical limitations.
(4) Acquire the ability to implement deep learning using Python.
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 of this lecture has been conducting research on artificial intelligence and mathematical optimization for 10 years, and at the same time, has been developing software for machine learning at a company he founded.
Keywords
Deep learning, image recognition, natural language processing, Python, programming
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 and group work, using the browser-based development environment (Google Colab).
Course schedule/Objectives
| Course schedule | Objectives | |
|---|---|---|
| Class 1 | Guidance, trends in the development of artificial intelligence and various issues in the field of artificial intelligence | To understand the purpose of this lecture and the technological transition of artificial intelligence. Students will also understand various problems in the field of artificial intelligence, such as frame problems and symbol grounding problems. |
| Class 2 | Classification and evaluation metrics of machine learning methods | To understand the characteristics and classification of various artificial intelligence algorithms and the evaluation index of prediction accuracy. |
| Class 3 | Neural Networks and Deep Learning | To understand the basic principles and deepening of neural networks. |
| Class 4 | Time Series Data Processing (Programming) | To understand the principles and applications of recurrent neural networks; to implement a weather prediction AI using Python programming. |
| Class 5 | Computer Vision and Natural Language Processing | To understand the principles and applications of convolutional neural networks. To understand natural language processing such as neural machine translation and speech synthesis. |
| Class 6 | Bayesian statistics and machine learning, group work 1 | Deepen your understanding of Bayesians as implemented in spam filters, etc. |
| Class 7 | Group presentation | Present business ideas related to artificial intelligence. |
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.
Related materials will be distributed during the lecture.
In addition, the following books may be referred to as necessary.
Uji Igari et al., Deep Learning Textbook: G-test (Generalist) Official Textbook, 2nd Edition, Shoei-sha, April 27, 2021.
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.A539 ： Advanced Methodology of Mathematical and Computational Analysi II
Prerequisites
Nothing
Other
Please check T2SCHOLA for class materials.