Machine Learning: Theory and Application

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.

Socio-cultural program of extracurricular activities and networking events are included*::

• Online Interactive Campus Tour 
• Online broadcasting of excursion to the Hermitage museum; 
• Online Pub Quiz.

*All of the listed above activities will to take place but in case any of those will have to be cancelled, an alternative event will be offered to participants.

• Elementary knowledge of programming skills;
• Basic Python skills (classes, conditional constructs, functions, loops, methods, syntax);
• Knowledge of basics of matrix operations and differentiation;
• Good command of English. All classes and extracurricular activities are conducted in English. Knowledge of the Russian language is not required;
• Applicants are expected to have at least 1 year of University level studies.

Anaconda (free program)

The link: https://docs.anaconda.com/anaconda/install/

Please, install the program before the course starts.

The syllabus includes:

• Introduction to Artificial intelligence and Machine Learning;
• Brief History review 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.