Master’s Programme "Math of Machine Learning" (previously - “Statistical Learning Theory”)

Objectives

The objective of the master's program is to familiarize students with the most relevant results in the field of statistical theory of learning and its applications, the necessary mathematical apparatus, as well as the development of students' skills to conduct independent scientific research in this field.

 To develop students' skills as a developer and researcher in the field of statistical theory of learning to a level that meets the highest professional requirements imposed by leading domestic and foreign companies and scientific and educational centers.

 To develop students' leadership qualities and skills to apply the acquired fundamental and applied knowledge to select an urgent task (which can be solved within the framework of the concept of statistical theory of learning), develop algorithms for solving this problem, organize the development process, including with the involvement of highly specialized specialists in various fields.

 To teach students to develop mathematical and simulation models for solving machine learning problems, to use standard computer tools to study the effectiveness of their work, to use professional search engines, to present the results in the form of publications and reports, to make expert assessments.

Courses taught by lecturers

• Markov Chains 

 Lecturers: Goldman Artur, Samsonov Sergey

• Modern Algorithmical Optimization 

 Lecturers: Dvinskih Darina

• Mathematical Foundations of Reinforcement learning

 Lecturers: Levin Iliya, Naumov Alexey

• High Dimensional Probability and Statistics

 Lecturers: Noskov Fedor, Naumov Alexey, Quentin Paris

• Mentor's Seminar "Math of Machine Learning"

 Lecturers: Noskov Fedor, Naumov Alexey

• Additional Topics in Probability Theory 

 Lecturers: Samsonov Sergey, Ulianov Vladimir