11, Pokrovsky boulevard.
Phone: +7 (495) 531-00-00 *27254
Rodomanov A., Kropotov D.
SIAM Journal on Optimization. 2020. Vol. 30. No. 3. P. 1878-1904.
A. Boldyrev, D. Derkach, F. Ratnikov et al.
Journal of Instrumentation. 2020. Vol. 15. P. 1-7.
Dvurechensky P., Eduard Gorbunov, Gasnikov A.
European Journal of Operational Research. 2021. Vol. 290. No. 2. P. 601-621.
Kuznetsov S., Kaytoue M., Belfodil A.
In bk.: International Journal of General Systems. Iss. 49. 2020. P. 271-285.
Kaledin M., Moulines E., Naumov A. et al.
In bk.: Proceedings of Machine Learning Research. Vol. 125: Proceedings of Thirty Third Conference on Learning Theory. 2020. P. 2144-2203.
The Faculty of Computer Science was created with the goal of becoming one of the world’s leading faculties for developers and researchers in data analysis, machine learning, big data, theoretical computer science, bioinformatics, system and software engineering, system programming, and distributed computing. In cooperation with major companies like Yandex, Sberbank, SAS, Samsung, 1C, and many others, the Faculty provides both deep theoretical knowledge and hands-on practical experience in many branches of contemporary computer science.
From late March and early April, HSE will offer four new English-taught coursers on Coursera on intercultural communication, machine learning, computer vision, and stochastic processes.
On March 29, an English-taught course ‘Introduction to Multilingual and Multicultural Education’ was launched. It deals with linguistic diversity, multiculturalism, and the problems which are encountered by education system in relation to these areas. The processes of globalization are highly complex and influence the multi-layered structures of societies, including the economic, socio-cultural, political, linguistic and education aspects. A remarkable increase in global migration flows, especially over the last decade, means that host countries have to to pay particular attention to providing equal education opportunities.
Furthermore, on today’s highly competitive labour market, learning global/international languages (e.g. English) is seen as a necessity. How and at what age should children start learning an additional language (or languages)? Do bilingual/multilingual children perform better at school than their monolingual peers? Which language/education policies are successful and which are not? What challenges do students and teachers face in a multilingual/multicultural classroom?
The lecturer, Denis Zubalov, Assistant Professor at the School of Philology, will address these and many other issues. By the end of this course, you will be able to critically evaluate various teaching practices in multilingual and multicultural settings and possess valuable insights into the learning needs of students in a multilingual/multicultural classroom. The course lasts eight weeks, with 4-6 hours of study per week.
Enrolment is open until April 3.
If you are interested in machine learning, we recommend that you enroll in the course ‘Practical Reinforcement Learning’ offered by the HSE Faculty of Computer Science. Reinforcement learning (RL) is a prominent area of modern research in artificial intelligence. Students will learn develop both theoretical core and recent practical RL methods. The students will be guided through the basics of RL: we will talk about essential theory of RL, value-based methods (such as SARSA and Q-learning) and policy based algorithms and methods designed to solve the optimal exploration problem.
The course lecturers, Pavel Shvechikov and Alexander Panin from the HSE Faculty of Computer Science, will also present useful practical tips and tricks needed for stabilization, and for applying the methods to large scale problems with deep neural networks.
Preliminary launch date: April 12. The course is English-taught.
Deep learning has boosted the already rapidly developing field of computer vision. With deep learning, many new applications of computer vision techniques have been introduced and are now becoming part of our everyday lives. These include facial recognition and indexing, photo stylization and machine vision in self-driving cars.
The goal of the course ‘Deep Learning in Computer Vision’ is to introduce students to computer vision, starting from the basics and then turning to more modern deep learning models. The course lecturers, Anton Konushin, Associate Professor at the Big Data and Information Retrieval School, and Alexey Artemov, Associate Professor at the HSE Joint Department with Yandex, will cover image and video recognition, object recognition and image search, various object detection techniques, motion estimation, object tracking in video, human action recognition, and finally image stylization, editing and new image generation.
The course is taught in English. Duration is five weeks.
Preliminary launch date is April 16; please see the information on the course page.
In April, ‘Stochastic Processes’, a new online course by Vladimir Panov, Assistant Professor at the Department of Statistics and Data Analysis, will be launched. The purpose of this course is to equip students with theoretical knowledge and practical skills necessary for the analysis of stochastic dynamical systems in various fields.
You’ll study the basic concepts of the theory of stochastic processes, the most important types of stochastic processes, as well as the methods for describing and analyzing complex stochastic models. As part of the practical training, you’ll learn to understand the most important types of stochastic processes (Poisson, Markov, Gaussian, Wiener processes and others) and will be able to find the most appropriate process for modelling in economics, engineering and other fields. You’ll also study the notions of ergodicity, stationarity, and stochastic integration, and learn to apply these terms in the context of financial mathematics.
The online course ‘Stochastic Processes’ provides the necessary theoretical basis for studying other courses in stochastics, such as financial mathematics, quantitative finance, stochastic modeling and the theory of jump - type processes. It is assumed that the students are familiar with the basics of probability theory. Knowledge of the basics of mathematical statistics would be a plus, although not necessarily required. The course is taught in English and lasts eight weeks.
All HSE courses on Coursera are re-launched regularly. Please follow the announcements on the eLearning Office pages on Facebook and VK.