The course is devoted to modern generative models (mostly in the application to computer vision).
We will study the following types of generative models:
- autoregressive models,
- latent variable models,
- normalization flow models,
- adversarial models,
- diffusion and score models.
Special attention is paid to the properties of various classes of generative models, their interrelationships, theoretical prerequisites and methods of quality assessment.
The aim of the course is to introduce the student to widely used advanced methods of deep learning.
The course is accompanied by practical tasks that allow you to understand the principles of the considered models.
- telegram: @roman_isachenko
- e-mail: [email protected]
| # | Date | Description | Slides |
|---|
- 6 homeworks each of 13 points = 78 points
- oral cozy exam = 26 points
- maximum points: 78 + 26 = 104 points
- probability theory + statistics
- machine learning + basics of deep learning
- python + pytorch