This discipline provides for the study of the basic concepts of parallel computing, necessary for the subsequent study of models, methods and technologies of parallel programming. In a simple and understandable form, the course provides examples of modern high-performance computing systems, discusses ways to analyze the complexity of calculations and assess the possibility of their parallel execution, provides the basics of developing parallel methods. The purpose of the discipline is to familiarize students with the totality of information processing tools and personnel combined to achieve certain goals.

The course examines the use of neural networks for object management, the use of modern models of artificial neural networks for the application and processing of information. As a result of training, the doctoral student develops the skills to develop artificial neural network architectures, to be able to design systems with artificial intelligence based on the network.

Creation and application of algorithmic, hardware and software systems for automation, management and control of technological processes and production, ensuring the production of high-quality, safe, competitive products freeing a person completely or partially from direct participation in the processes of obtaining, transforming, transmitting, using and managing production.

The purpose of the discipline is to train specialists who are able to assess the impact of big data technologies on the activities of large enterprises. As a result of the training, the student will be able to offer options for the effective use of big data technologies in enterprise management, perform analysis and processing of big data in real time, as they arrive.