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Winter SchoolMachine Learning: Theory and Application

Machine Learning: Theory and Application

Winter School - Online/Tailor-made
January 16 - January 27, 2023

The course will be held online but 2 participation options are available for certain individuals:

Option 1 - online.

Option 2 – 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 Winter School!

Machine_Learn
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.

REGISTER ONLINE

Duration: 2 weeks

ECTS credits: 4.0

Participation fee: 30 000 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.

Accommodation

Provided only for the Tailor-made format:

  • 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.

Deadline for registration: December 22, 2022

If you have taken the Data Analytics interseasonal course, there are no entrance requirements for you but the last two ones.

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

Machine Learning: Theory and Application.pdf

REGISTER ONLINE

  • 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