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School of Engineering
- Undergraduate major in Mechanical Engineering
- Undergraduate major in Systems and Control Engineering
- Undergraduate major in Electrical and Electronic Engineering
- Undergraduate major in Information and Communications Engineering
- Undergraduate major in Industrial Engineering and Economics
- Common courses
- School of Materials and Chemical Technology
- School of Computing
- School of Life Science and Technology
- School of Environment and Society
- 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
-
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 Electrical and Electronic Engineering
- Instructor(s)
- Wakabayashi Hitoshi Sawai Ryo Mitsufuji Yuki
- Class Format
- Lecture (Face-to-face)
- Media-enhanced courses
- Day/Period(Room No.)
- Tue3-4(S422)
- Group
- -
- Course number
- EEE.S571
- Credits
- 1
- Academic year
- 2022
- Offered quarter
- 3Q
- Syllabus updated
- 2022/9/9
- Lecture notes updated
- -
- Language used
- English
- Access Index
-
Course description and aims
AI & network communication systems are expected to be applied in various fields as core technologies for realizing the evolution of various industries and the creation of new business. In this course, leading researchers from companies are invited as lecturers to learn about AI & network communication technologies that are being used at the forefront of actual business from the basics to their applications.
Student learning outcomes
Through this course, students will gain a basic understanding of cutting-edge system design technology (especially AI & network communication systems) that will be needed in society in the future, as well as a deeper understanding of each application case, as well as a better understanding of the mindset and workplace atmosphere required to work as a researcher and engineer in a company.
Course taught by instructors with work experience
| ✔ Applicable | How instructors' work experience benefits the course |
|---|---|
| In this lecture, teachers in charge of education who have practical experience in the field of network communication and AI technology use practical experiences to teach its basic and applications. |
Keywords
AI, machine learning, generative models, time series modeling, music generation, IoT, 5G, Beyond 5G (6G), Wireless LAN, LPWA
Competencies that will be developed
| ✔ Specialist skills | Intercultural skills | Communication skills | Critical thinking skills | ✔ Practical and/or problem-solving skills |
| ✔ Expertise, development skills (practical or solution skills) | ||||
Class flow
Lecture in-person
Course schedule/Required learning
| Course schedule | Required learning | |
|---|---|---|
| Class 1 | Introduction: Overview of AI and Network Communication Systems | Getting familiar with the recent trends in the field of AI and network communication systems |
| Class 2 | Network-1: Fundamentals on network communication technology | Revisits on the basis for better understandings on advanced network communication technology trends |
| Class 3 | Netowrk-2: System architecture design for network communication | Deeping basic knowledges for commercial system deployments i.e. 5G and Wi-Fi from network system architecture perspectives |
| Class 4 | Network-3: Advanced network communication technology/application/service integration | Touching upon future radio/network access technologies involving AI applications and other advanced technologies |
| Class 5 | AI-1: Fundamentals on Deep Generative Models | Understanding the basics of generative modeling and its deep-learning-based variants |
| Class 6 | AI-2: Applications to Audio Restoration | Understanding the recent advancement of AI-based audio restoration and how the technology is used in commercial applications |
| Class 7 | AI-3: Applications to Music Generation | Understanding the recent advancement of AI-based music generation and the ethical issues of generated contents |
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)
Distribute at the beginning of each round (also upload material to T2SCHOLA)
Reference books, course materials, etc.
Jakub M. Tomczak, “Deep Generative Modeling”, Springer
Andreas F. Molisch, “Wireless Communications”, WILEY
Assessment criteria and methods
No tests are conducted, and subject reports are submitted
Related courses
- ICT.H503 ： Speech Information Technology
- ICT.H416 ： Statistical Theories for Brain and Parallel Computing
- ICT.A402 ： Communications and Computer Engineering I
- ICT.S407 ： Wireless Signal Processing
Prerequisites (i.e., required knowledge, skills, courses, etc.)
Nothing special. Students interested in industrial communications and AI technologies
Other
We welcome not only those who are concerned about communication technology and AI technology, but also those who want to hear about R&D in companies, and those who want to get information that will help them think about their future careers. * DE is a title given to technical experts, to which only 40 people are appointed across Sony.
