Master’s programme 'Statistical Learning Theory'
This new joint programme trains the next generation of scientists to effectively carry out fundamental research and work on new challenging problems in statistical learning theory. This field is at the cutting edge of various disciplines of mathematics and computer science. It is one of the most dynamic areas of modern science, encompassing mathematical statistics, machine learning, optimization, and information and complexity theory. From the start of the programme, students collaborate in thematic working groups and actively participate in research, learning from HSE and Skoltech scientists as well as leading global specialists in statistics, optimization and machine learning.
Programme Overview
This programme stands at the crossroads of various disciplines of modern mathematics and computer science, including statistics, optimization, learning theory, information theory, complexity theory, as well as at the intersection of science and innovation in the field of modern information technology. Leading experts at HSE and Skoltech jointly provide instruction in this unique research-driven programme.
Students participate in one or more working groups (research seminars), where they determine focus areas for an initial survey report and then solve challenges at the intersection of cutting-edge research and technology in statistical learning theory. These seminars are built on teamwork, as the tasks undertaken are so complex that they can’t be solved by one person alone. Students learn how to effectively collaborate, bringing together their diverse collective skills, competencies, and experiences to determine successful solutions for complicated issues.
Programme courses are taught by leading HSE experts, including globally renowned scholars such as Dr. Yurii Nesterov, Dr. Denis Belomestny, Dr. Dmitry Vetrov, Dr. Andrei Sobolevski, Dr. Alexey Naumov, and Dr. Quentin Paris. Lectures are also delivered by Skoltech professors including Dr. Ivan Oseledets, Dr. Viktor Lempitsky, Dr. Evgeny Burnaev, and Dr. Yury Maximov. This team is rather young, but its members have already made significant research achievements.
The programme actively cooperates with the Russian Academy of Sciences Institute for Information Transmission Problems, as well as with relevant faculties at Moscow State University and the Moscow Institute of Physics and Technology. Graduates go on to work for major Russian and international companies and are in high demand for their exceptional mathematical skills.
Required:
- Application form
- Bachelor’s (Specialist’s or Master’s) diploma and official transcripts of previous educational studies. (if you have not yet received your Bachelor’s diploma, please include an official copy of your most recent academic transcript).
You need to have a degree in Computer Science or Applied Mathematics and Informaticsor at least to have courses in Algorithms and Data Structures, Programming, Databases Theory and Advanced Mathematics (Calculus, Linear Algebra, Probability Theory and, Mathematical Statistics) in your diploma or certificates.
The Admission Committee takes into consideration the number of hours, the final assessments and your university ranking.
- CV, which confirms your professional experience (please indicate your position and the list of duties) or scientific activity in the the Applied Mathematics and Informatics field of study, including educational internship.
- Letter of motivation (~500 words, not more than one page of printed text), describing your reasons for applying in the context of your long-term career goals and background. The quality of your English is also evaluated.
- At least one letter of recommendation. Please provide your recommenders with HSE’s letter of recommendation guidelines.
- Exam results confirming language proficiency. Valid IELTS certificate (>= 5.5), TOEFL IBT(>= 70), TOEFL PBT (>= 500).
For more information please click "Further Official Information" below.
This opportunity has expired. It was originally published here:
https://www.hse.ru/en/ma/sltheory/