Educational activity

Over the years Bayesian methods research group, on the basis of which a Centre was established, collected expertise in the field of Bayesian methods, deep learning and optimization techniques. This knowledge formed the basis of several training courses and allow the Centre staff to explain material in clear language to students. The courses follow proven format that involves active work of students during the semester and aims at students ' understanding of the subject, not the memorization of basic facts. 

Courses taught by the Centre staff:

1. Deep Learning in Sound Processing (Lecturer: Aibek Alanov)

2. Text Analysis. Generative Models (Lecturer: Mishan Aliev)

3. Probability Theory (Lecturer: Nikita Morozov)

4. Bayesian Methods in Machine Learning (Lecturers: Dmitry Vetrov, Egor Chimbulatov)

5. Deep Learning for Text Data (Lecturer: Alexander Shabalin)

6. Probability Theory (advanced course) (Lecturer: Denis Rakitin)

Centre scientists manage projects of undergraduate students. D. Vetrov is a scientific advisor of several Ph. D. students.

The Centre organizes research events. Materials from previous events are available here.