Basics of artificial intelligence

The aim of the course is to provide an insight into the basic procedures of quantitative information processing obtained in research results for programming engineers, using modern information technologies and learning their practical application. To give an initial idea about artificial neural networks and their application possibilities, to be able to apply the use of artificial intelligence methods in real situations.

Deep Learning


Machine learning is the process of developing and applying predictive algorithms to predicting future outcomes using available training data. Machine learning is a corner-stone for Data science, Big Data analytics, Robotics, Natural language processing and AI in general. Course introduces students to supervised and unsupervised machine learning concepts and algorithms from basic classification to Deep Learning with artificial neural networks. As computers and GPUs become more powerful, Deep Neural Networks are gradually taking over from simpler Machine Learning methods.

Parallel Algorithms


Course should give insight into the principles of design of parallel algorithms and overview of some standard parallel algorithms. The course will teach basic theoretical models of parallel computing and performance and complexity metrics of parallel algorithms. The course will also provide overview on the current industrial parallel computing systems and programming languages.

Applied Deep Learning


This course is intended to provide an overview of modern applications of machine learning and develop practical skills in using deep neural networks for common machine learning tasks. The course provides an introduction into artificial neural network based models, as well as an introduction to existing API frameworks for training such models. Previous knowledge regarding machine learning is not expected. The practical assignments will be developed in Python programming language.

Mathematical and statistical software packages


The course introduces students to the basic principles that should be acquired to mathematicians, statisticians, economists and financiers and others in order to use software packages R, SPSS and MATLAB for their scientific research and practical work. An overview is given about the appropriate application features and options of the software packages R, SPSS and MATLAB focusing primarily on the students’ needs to work out different practice, bachelor and other scientific works.