St. Petersburg, Russia

Machine Learning Theory and Application

blended course
when 17 January 2022 - 28 January 2022
language English
duration 2 weeks
credits 4 EC
fee EUR 270

The course introduces students to the theoretical foundations of machine learning and data science, as well as to the solution of real business problems with the help of computer vision, classification and regression algorithms. The optimal balance between theory and practice provides both a good foundation and the ability to apply knowledge in practice.

Online lectures will be delivered synchronized as live talk with professors and groupmates. Records of classes will be available on SPbPU platform for 1 month after the course end.

Online format:*

- Online Pub Quiz;
- Online Interactive Tour to SPbPU Museum;
- Online broadcasting of excursion to the Hermitage museum;
Cultural program in the Hybrid format is discussed with participants individually.

*All of the listed above activities are planned to take place but in case any of those will have to be cancelled, an alternative event will be offered to participants.

Course leader

Ogul Unal - PhD, Institute of Computer Science and Technology, SPbPU; M-com Search Engine Optimization specialist”;
Nikita Kudryashov – PhD, Institute of Computer Science and Technology, SPbPU; Gazprom-neft leading specialist.

Target group

Entrance requirements
- Elementary knowledge of programming skills;
- Knowledge of basics of matrix operations and differentiation;
- Good command of English. All classes and extracurricular activities are carried out in English. Knowledge of the Russian language is not required;
- Applicants are expected to have at least 1 year of University level studies.

Fee info

EUR 270: Online format: 270 Euro

Hybrid format: 270 Euro + 4000 Rub (non-refundable registration fee for the Letter of Invitation)

Participation fee includes tuition fee, study materials, field trips and cultural program.

Upon successful completion of the course students will receive hard copies of certificates with ECTS credits (mailed by post in case of the online format of the Winter School).