Math of Machine Learning

During the two years you spend in the masters (MSc) programme, which is taught entirely in English, you will take a series of basic and elective courses on the subjects studied by the members of the faculty.

Basic courses include:

• Modern Methods of Data Analysis: Stochastic Calculus
• Numerical Linear Algebra
• Modern Methods of Decision Making: Advanced Statistical Methods
• Machine Learning
• High-dimensional Statistical Methods
• Modern Algorithmical Optimization

Elective courses include:

• Introduction to Data Science
• Efficient Algorithms and Data Structures
• Digital Image Processing
• Information and Coding Theories
• Deep Learning
• Geometrical Methods of Machine Learning
• Bayesian Methods for Machine Learning
• Random Matrix Theory
• Neurobayesian Models

Unique in Russia, the joint HSE-Skoltech masters programme in Math for Machine Learning offers students unparalleled opportunities to learn from leading experts on topics such as algorithmic complexity, conic optimization, stochastic optimization, online optimization, combinatorial optimization, and algorithms for convex optimization.

Partnerships 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, provide additional opportunities for students to make the most of their time in the programme.

As a student in the joint HSE-Skoltech masters programme, you will have a unique opportunity to join one of the existing research groups led by outstanding researchers from HSE and Skoltech. You will also be able to participate in one or more working groups (research seminars). These seminars are built on teamwork, as the tasks undertaken are so complex that they can’t be solved by one person alone.

You will learn how to effectively collaborate with fellow students and researchers, who bring together diverse collective skills, competencies, and experiences that are essential for determining successful solutions to complicated issues.

Graduates of the joint HSE-Skoltech masters programme in Math for Machine Learning go on to work for major Russian and international companies that have high demand for employees with exceptional mathematical, computer science, and data analysis skills, for example. Representative companies include Yandex, Google, Microsoft, Bosch, Huawei, and Siemens, for example.

Graduates pursing practice-focused careers typically concentrate in the following fields:

• industry analysis
• consulting
• association and foundation work
• government
• banking and investment
• data science and analytics
• transport planning
• management

Students looking to pursue an academic career can rest assured that they will be well positioned to continue with their studies at leading global and Russian centres of applied mathematics, mathematical modelling and computer science. In previous years, graduates have continued their studies at Humboldt University (Berlin), Catholic University of Louvain (Belgium), Joseph Fourier University (Grenoble), Max Planck Institute for Mathematics (Bonn), University of Mannheim, ENSAE ParisTech (Paris), and Steklov Mathematical Institute (Moscow).

Applicants to the programme will need to apply separately to HSE and Skoltech and will be selected on the basis of a portfolio competition. Detailed information on portfolio requirements can be found here.

Interested students can access instructions on How to Apply along with general information on international admissions.

Once your application is submitted, you may be contacted by a programme coordinator to schedule an interview.