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 International Party; 
• Online Pub Quiz; 
• Online Interactive Campus Tour with international buddies from PolyUnion club; 
• Online broadcasting of excursion to the Hermitage museum.

*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.

• 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 2 years 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;
• History of AI and state of the art;
• Python tools for Machine learning;
• Machine learning project structure;
• Supervised and unsupervised learning;
• Overfitting and underfitting;
• Model Validation techniques;
• Machine learning algorithms classification;
• Data processing techniques;
• Binary classification and logistic regression;
• Shallow Neural networks;
• Deep Neural networks;
• Convolutional Neural Networks Basics;
• Sequence models basics.