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.
- Required programs
- The following program is required for this course:
- Anaconda: Available at https://docs.anaconda.com/anaconda/install/ for all platforms.
- Please, install the program before the course starts.
- 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:
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