Special Courses and Electives

To give students theoretical knowledge and practical skills in using artificial intelligence in specialized fields, the "Intellect" Foundation supports the development and delivery of new semester-long educational courses.

The courses are open to all interested undergraduate, graduate, and PhD students of Lomonosov Moscow State University who meet the necessary prerequisites — both from the home faculty where the course is offered and from related fields.

All courses supported by the Intellect Foundation as part of the Grant Competition for Course and Elective Authors can be found in the Archive.

Neural Networks and Their
Application in Scientific
Research

The course "Neural Networks and Their Application in Scientific Research" was developed by staff of the Faculty of Physics at Lomonosov
Moscow State University with the support of the
"Intellect" Foundation.
Course Goal
To give young researchers from various faculties of Lomonosov Moscow State University practical skills in using classical machine learning methods and artificial neural networks for application in scientific research.
Enrollment in the course takes place once per semester. Outstanding students on the course are awarded a named scholarship. The 10 best authors of coursework projects also receive a grant.

All students on the course may take part in the final publication competition, where the authors of the 5 best papers will receive a grant for the best publication.
Detailed information
about the course is available at
msu.ai

IFC "Neural Networks
and Their Application in Scientific Research"

The course "Neural Networks and Their Application in Scientific Research" was developed by staff of the Faculty of Physics at Lomonosov
Moscow State University with the support of the
"Intellect" Foundation.
Course Goal
To give young researchers from various faculties of Lomonosov Moscow State University practical skills in using classical machine learning methods and artificial neural networks for application in scientific research.
Enrollment in the course takes place once per semester. Outstanding students on the course are awarded a named scholarship. The 10 best authors of coursework projects also receive a grant.

All students on the course may take part in the final publication competition, where the authors of the 5 best papers will receive a grant for the best publication.
Detailed information
about the course is available at
msu.ai
  • About the Course

    The interfaculty course "Neural Networks and Their Application in Scientific Research" was developed based on the curriculum of the course of the same name for young researchers.
    Computer vision, speech recognition, text "understanding," and many other forms of narrow artificial intelligence (ANI – artificial narrow intelligence) are now part of everyday life. These technologies are built on machine learning, which is used to construct algorithms capable of learning. At present, deep learning (neural networks) is considered one of the most promising machine learning methods. Over the past few years, deep learning has found applications in nearly every field of science, from biology and physics to linguistics and philosophy.

  • Study Format

    This 12-week course will give students a high-level overview of modern artificial intelligence methods and their applications in various scientific fields. Demonstrations and quizzes will help students understand what is possible today and what is likely to become possible in the near future. 

  • Who the Course Is For

    Thanks to its simplified presentation, this interfaculty course is suitable for students who are just starting to explore the topic of artificial intelligence. Students from any faculty of Lomonosov Moscow State University (except the Faculty of Physics) can register for the course and receive credit.
    Lecture materials are available to anyone interested in self-study. They can be found at https://msu.ai/mfk

Spring Semester

2024/2025

Fall Semester

2025/2026

Applications for the fall semester of the 2025/2026 academic year are closed;
no competitive selection is being held