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:
- 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.
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