The goal of the course is to introduce the students to the concepts of scientific programming, applications of the current computing architectures and platforms (grid, HPC) in science and engineering, advanced numerical algorithms

Research generates significant amounts of data that are used to communicate the results of a particular investigation. However, currently, these data are usually unstructured and highly scattered. As a result, this data can not be used to verify, replicate or reanalyze the findings. Moreover, different standards, annotation practices and data formats for data and metadata (if available) might have been used by other researchers, introducing additional heterogeneity and difficulties in later data integration and interpretation.

The student will be capable to analyse and design parallel architectures and programmes using various methods and techniques.

The aim of the course is to create an insight into high-performance computing in Physics. The tasks of the course are: (1) to overview applications of parallel algorithms in Physics problems, (2) to overview methods of parallel computing, (3) to learn how to use high-performance libraries, (4) to analyse efficiency of parallel algorithms, (5) to gain an experience in using supercomputing centres.

The students will have an in depth understanding of the machine learning techniques used on structured data (input and output). They will be able to successfully apply machine learning algorithms when solving real problems concerned with computational biology, multimedia systems and social networks. They will be able to concept, analyze, realize and evaluate the developed machine learning system performances.

The aim of the course is to acquaint students with creation of mathematical models for description of complex physical processes and methods for solving the corresponding problems of mathematical physics, using different program tools – both commercial and specialised mathematical modelling programs.

The study course covers the theoretical and practical principles of high-performance computing in the context of cybersecurity by using graphics processing hardware and dedicated software. The study course includes an overview of architecture, computing algorithms, software libraries and tools of parallel computing platform CUDA based on graphics processors. An in-depth attention is devoted to the cross-disciplinary application of CUDA in the areas of vulnerability analysis, parallel data encryption, surveillance data mining, object detection and recognition.

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.

The goal of the course is to understand the methods of distributed and parallel processing, the possibilities to paralelise sequential execution, distributed processing of big data and the problems that need to be overcomed.

The course is devoted to the construction of artificial neural networks and includes the following sections: Biological neuron; Artificial neuron; Single layer perceptrons; Straightforward chain networks; Architecture; Learning procedures in single-layer and multi-layer networks; Backward chain networks; Adaptation procedures; Associative memory; Concurrent learning; Software; Neural network applications.

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.