Тo train the students for building advanced intelligent systems with image and sound processing, while solving problems of pattern recognition, classification, ranking in various domains: medicine, engineering, molecular biology. Candidates will be able to design, implement and evaluate the performance of advanced systems using machine learning
The goal of the course is to introduce the students with the concepts of grid computing, collaborative research, programming large scale systems, high performance computing, distributed storage, current trends in the systems with high degree of parallelism.
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.
