UNIVERSITY OF TOKYO · JAPAN
Deep Generative Models Seminar
Certificate · Taught in en
At a glance
3 mo
Duration
—
IELTS
€0k
Tuition / yr
en
Language
Certificate
Level
- 10 sessionsCourse Duration
- Fully OnlineDelivery Mode
- Hands-on ExercisesLearning Method
- Diffusion Models CoveredKey Topics
- Free for studentsTuition Fees
This is a 10-session, practical seminar focused on "Deep Generative Models," the core technology behind generative AI. The course starts with fundamental knowledge of generative models and progresses to cover various advanced topics, including the latest diffusion models. Each session includes hands-on exercises to deepen understanding through practical application. The content is supervised and developed by the Matsuo-Iwasawa Laboratory at the University of Tokyo, which has over 10 years of experience running Data Scientist training and Deep Learning courses, fostering over 55,000 individuals. The seminar is conducted entirely online.
Curriculum
The seminar consists of 10 sessions held weekly. While live sessions are conducted via Zoom, archived videos will be available. Participants are expected to complete assignments and achieve a certain score to successfully complete the course. The curriculum covers a range of topics from fundamental generative models to advanced techniques, with a special focus on diffusion models.
- Session
- Introduction to Generative Models
- Latent Variable Models and Mixture Models
- VAE (Variational Autoencoder)
- GAN (Generative Adversarial Network)
- Energy-Based Models
- Autoregressive Models and Flow-Based Models
- Neural ODE and Continuous Normalizing Flows
- Score-Based Models and Diffusion Probabilistic Models
- Diffusion Models and Flow Matching
- Advanced Topics in Diffusion Models
Frequently asked questions
- What are the tuition fees for the Deep Generative Models Seminar?
- Tuition fees are free for students. There might be paid courses available for non-students.
- What is the duration and format of the Deep Generative Models Seminar?
- The seminar consists of 10 sessions and is conducted entirely online, with live sessions via Zoom and archived videos available.
- What are the entry requirements for this seminar?
- Applicants must be currently enrolled in a university or equivalent institution with an expected graduation date on or after July 28, 2026. Foundational knowledge of Deep Learning, mathematics, and Python for numerical analysis is also required.
- How do I apply for the Deep Generative Models Seminar and what are the deadlines?
- You must first register for an ID on the Omnicampus platform by July 10, 2026, and then apply for the course by July 13, 2026. Selection results will be notified by July 21, 2026.
- What kind of career opportunities can completing this seminar lead to?
- Completing the seminar can lead to further opportunities such as invitations to events, study groups, and research projects, potentially serving as a stepping stone for career advancement in AI-related fields.
Entry requirements
Student StatusCurrently enrolled in a university, junior college, technical college, or vocational school, with expected graduation on or after the course start date (July 28, 2026). Prospective students starting in 2026 are also eligible if they can prove their student status. Socially engaged students and those studying abroad are also welcome. This does not apply to auditors or single-course students.
Prerequisites include foundational knowledge of Deep Learning with the ability to implement basic models, understanding of linear algebra, calculus, and probability/statistics at a university science department level, and experience with Python for numerical analysis. Participants must be able to reliably watch lectures, dedicate at least 3 hours per week for self-study, and have a PC capable of running Zoom, Google services (Drive, Forms, Colab), and a web browser. A lottery may be held if there are too many applicants.
Fees
Tuition (international)Free for students
Tuition (home)Free for students
LivingNot specified
Tuition fees are free for students. There might be paid courses for non-students.
How to apply
1. Register for an IDVisit the Omnicampus student sign-up page and create your account. You will need to provide your student information, including a valid graduation date and upload a copy of your student ID. Ensure your email address is correct and check for confirmation emails in your inbox or spam folder. If you already have an ID, log in to your existing account.
2. Apply for the CourseOnce your ID is registered and you are logged in, navigate to the specific course application page and submit your application. Make sure to fill out all required fields accurately.
Scholarships
Asian Development Bank-Japan Scholarship Program (ADB-JSP)Covers round-trip travel, entrance and tuition fees, subsistence allowance (147,000 Yen/month), and other allowances.
ITO Foundation for International Education ExchangeNot specified
Accreditation
No university credits are awarded for this public course.
Frequently asked questions
What are the tuition fees for the Deep Generative Models Seminar?
+
Tuition fees are free for students. There might be paid courses available for non-students.
What is the duration and format of the Deep Generative Models Seminar?
+
The seminar consists of 10 sessions and is conducted entirely online, with live sessions via Zoom and archived videos available.
What are the entry requirements for this seminar?
+
Applicants must be currently enrolled in a university or equivalent institution with an expected graduation date on or after July 28, 2026. Foundational knowledge of Deep Learning, mathematics, and Python for numerical analysis is also required.
How do I apply for the Deep Generative Models Seminar and what are the deadlines?
+
You must first register for an ID on the Omnicampus platform by July 10, 2026, and then apply for the course by July 13, 2026. Selection results will be notified by July 21, 2026.
What kind of career opportunities can completing this seminar lead to?
+
Completing the seminar can lead to further opportunities such as invitations to events, study groups, and research projects, potentially serving as a stepping stone for career advancement in AI-related fields.