About

The course is devoted to modern generative models (mostly in the application to computer vision). 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.

Syllabus

• Density estimation task.
• Аutoregressive models.
• Latent variable models.
• Variational inference.
• Normalization flow models.
• Adversarial models.
• Diffusion models.

Labworks

6 homeworks: theory and practice.

Grading

Each homework gives 13 points + an exam for 26 points. Final score: (number of points / 8) - 2.

Prerequisites

Statistics, machine learning, deep learning, intro to bayesian inference.