International Polytechnic Winter School 2025 will be on campus
Do you want to go deeper to machine learning? Join this Winter School!
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
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.
Details of the options and booking procedures will be discussed with each applicant individually.
Deadline for registration: December 01, 2024
Cultural program
We offer our students excursions to the most famous palaces, monuments, museums of St. Petersburg, as well as other cultural activities.
The detailed course description for ECTS credits transfer at your home university:
Machine Learning: Theory and Application.pdf
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.
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.
Contacts:
Summer and Winter Schools Team
