The student will be introduced to the variety of modern intelligent systems and the ability to extract and store knowledge.

The goal of the course is to study approaches and solutions to overcome the challenges of modelling, management, implementation and deployment of big data production systems.

The student will be able to apply advanced algorithms and techniques from the area of AI and ML.

The goal of this course is to obtain knowledge and skills related to the modern trends in parallel processing, grids, HPC and cloud computing.

The student will have knowledge about paralel algorithms, parallel architectures, multithreading systems with the ability to create parallel applications and implement optimal deep learning algorithms.

The student will acquire knowledge about the five basic data analysis parts: data wrangling, clearning and sampling, data management, data analysis, prediction using statistical methods and data visualization.

By completing the course the student will be able to understand complex distributed and parallel systems and develop distributed applications including their optimisation.

The course introduces students to the theory and application of data visualization. Upon completion the student will be able to demonstrate knowledge of the concept of data visualization, choose and implement visualisation algorithms and use data visualization tools.

The goal of the course is to complete the knowledge of students in the field of intelligent systems, starting from pre-processing data to validation of the built system. Students will be able to build an intelligent system from start to finish on real domain specific problems.

The course will introduce the students data mining and machine learning algorithms for analyzing massive amounts of data. The emphasis will be on the distributed platforms and Map Reduce as a tool for creating parallel algorithms that can process large amounts of data.