Generative Models in Machine Learning

2024/2025

Academic Year

ENG

Instruction in English

Course type:

Elective course

When:

2 year, 1 module

Course Syllabus

Abstract

Deep generative models are widely used in many areas of applied machine learning. In this course, we will look at modern architectures of generative models and learning algorithms. The lectures will highlight the main approaches proposed, and analyze their main advantages and disadvantages. The seminars will cover examples of generating images, texts, and other objects using variational autoencoders (VAE), generative adversarial networks (GANs), autoregressive models, normalizing flows, and other approaches. The assignments in the seminars are motivated by well-known applications of generative models in science and industry.