Bachelor
2026/2027
Generative Models in Machine Learning
Type:
Elective course (Data Science and Business Analytics)
Delivered by:
Big Data and Information Retrieval School
Where:
Faculty of Computer Science
When:
4 year, 1, 2 module
Open to:
students of one campus
Language:
English
ECTS credits:
5
Contact hours:
56
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 by the beginning of 2021, 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.
Learning Objectives
- Introduce students with modern generative models
- learning how to use variational autoencoders to generate new objects
- learning to use generative adversarial networks to generate new objects
- learning to use normalizing streams for image generation
Expected Learning Outcomes
- Explains the choice of dimension of the latent space
- Able to train autoencoders
- Understands how to train variation act-coders
- Focuses on promising developments in the field of generative models
- Can apply normalizing streams to generate images
- Uses various quality metrics to validate generative models
- Understands the difference between generative and discriminative models
Course Contents
- Introduction to Generative Models
- Generative adversarial networks and variational autoencoders
- Reversible Models
- Diffusion Models
Bibliography
Recommended Core Bibliography
- Deep learning, Goodfellow, I., 2016
- Dhariwal, P., & Nichol, A. (2021). Diffusion Models Beat GANs on Image Synthesis.
- Глубокое обучение с точки зрения практика, Паттерсон, Дж., 2018
Recommended Additional Bibliography
- Глубокое обучение и TensorFlow для профессионалов : математический подход к построению систем искусственного интеллекта на Python, Паттанаяк, С., 2019