Machine Learning Theory and Application

Summer School - Online/Hybrid
July 19 - July 30, 2021

The course will be held online but 3 participation options are available for certain individuals:

Option 1 - online.

Option 2 - hybrid. Due to massive and continuous vaccination in Russia and resuming international air and railway service with 29 countries we offer a hybrid online/on-campus summer school option to students holding citizenship of:

the United Kingdom, Tanzania, Turkey, Switzerland, Egypt, Maldives, the United Arab Emirates, Kazakhstan, Kyrgyzstan, Republic of Korea, Cuba, Serbia, Japan, the Seychelles, Ethiopia, Vietnam, India, Qatar, Finland, Azerbaijan, Armenia, Greece, Singapore, Venezuela, Germany, Syria, Tajikistan, Uzbekistan, Sri Lanka.

You can join the online course for 270 euro (+4000 Rub for reg. fee) but come to St. Petersburg to take touristic advantages for the duration of the program and attend online classes at your dormitory/hostel.

If you choose Option 2, just apply for the online course here and inform us on your choice via email to get further instructions.

Option 3 – tailor-made. We may also arrange a tailor-made on-campus program for a group of minimum 10 students holding nationality of the countries Russia resumed international air and railway service with.

Do you want to go deeper to machine learning? Join this Summer School!


Machine Learning Theory and Application
Machine Learning Theory and Application
  • Brief description

    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.

Duration: 2 weeks  

ECTS credits: 4.0

Participation fee:

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 Summer School).

  • Cultural program

    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.


Provided only for the Hybrid or the Tailor-made formats:

  • on campus at the university dormitory
  • off-campus at partner hostels in the city center

Details of the options and booking procedures will be discussed with each applicant individually.

Machine Learning Theory and Application
Machine Learning Theory and Application

Deadline for registration

Online format: July 05, 2021

Hybrid format:

- for EU- or visa-free countries nationals: June 21, 2021

- for non-EU nationals: May 11, 2021

If you have taken the “Artificial Intelligence for All” interseasonal course, there are no entrance requirements for you, but the last two ones.

  • 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.
  • Course description

    The following topics will be covered:

    • - Introduction to Artificial intelligence and Machine Learning;
    • - Brief History rewiew and state of the art;
    • - Supervised and unsupervised learning;
    • - Overfitting and underfitting;
    • - Regularization in ML;
    • - Model Validation techniques;
    • - Machine learning algorithms classification;
    • - Data processing techniques;
    • - Machine learning application workflow;
    • - Hyperparameters tuning tactiques;
    • - Binary classification and logistic regression;
    • - Shallow Neural networks;
    • - Deep Neural networks;
    • - Convolutional Neural Networks Basics;
    • - Deep Sequential Neural Networks.

The detailed course description for ECTS credits transfer at your home university:

Machine Learning: Theory and Application.pdf


Professors and lecturers:

  • 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.


Summer and Winter Schools Team