Machine Learning: Theory and Application

Summer School - On campus
August 5 - August 16, 2024

The course also can be arranged as the tailor-made programe for a group of minimum 10 students upon request (the dates and terms can be discuss individually)

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.

Duration: 2 weeks

ECTS credits: 4.0

Participation fee: 50 000 RUB

Deadline for registration: May 27, 2024

  • Cultural program

    We offer our students excursions to the most famous palaces, monuments, museums of St. Petersburg, as well as other cultural activities.

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 ApplicationDescriptor_SS24.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; Senior Data Scientist, Rubbles company.


Summer and Winter Schools Team