Educational activity

Over the years of research, the group of Bayesian methods that formed the basis of the Centre has accumulated extensive expertise in the fields of Bayesian methods, deep learning, and optimization methods. This knowledge has formed the foundation for several educational courses and enables the Centre's staff to explain the material in a language accessible to students. The courses follow a format developed over recent years, which involves active independent work by students during the semester and is aimed at their understanding of the discipline, rather than memorizing key facts.

Educational courses taught with the participation of the Centre's staff:

1. 3D Computer Vision (Lecturer: Mishan Aliev)
2. Machine Learning 1 (Lecturer: Nikita Morozov)
3. Machine Learning 2 (Lecturer: Nikita Morozov)
4. Deep Learning 2 (Lecturer: Mishan Aliev)
5. Diffusion-Based Generative Models (Lecturers: Denis Rakitin, Alexander Oganov)
6. Probability Theory (Advanced Course) (Lecturer: Denis Rakitin)
7. Research Seminar "Machine Learning and Applications" (Lecturer: Nina Chelysheva)
8. Bayesian Methods for Machine Learning (Lecturers: Dmitry Vetrov, Egor Chimbulatov, Alexander Shabalin)
9. Deep Learning for Natural Language Processing (Lecturers: Egor Chimbulatov, Alexander Shabalin)
10. Natural Language Processing (Lecturer: Egor Chimbulatov)
11. Bayesian Methods for Data Analysis (Lecturer: Alexander Oganov)

The Centre's staff supervise bachelor's and master's students' course projects. The Head of the Centre, Aibek Alanov, is the academic supervisor for 5 PhD students at the Doctoral School of Computer Science. Under the supervision of the Centre's founder, Dmitry Vetrov, 11 dissertations for the PhD degree have been successfully defended; currently, 5 more PhD dissertations are being conducted under Dmitry Vetrov's supervision at HSE University.

The Centre organizes its own scientific events. Materials and video recordings of past events are available via the link.