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 students will attain in depth understanding of the machine learning and data mining techniques for massive data sets. They will be able to successfully apply machine learning algorithms when solving real problems concerning business intelligence, social networks, web data description. They will be able to concept, analyze, realize and evaluate the developed system performances.
The students will understand and be able to apply their knowledge about intelligent software algorithms.
The student will be capable to analyse and design parallel architectures and programmes using various methods and techniques.
Enabling the students to understand the technologies to discover and extract knowledge from data. The student will be capable to design and develop algorithms for information extraction from enormous amount of data.
This course covers the design and implementation of massive and parallel systems, both from machine (hardware) and program (software) sides. More often massive and parallel systems need high performance, scalability and portability, flexible and reusable components, which require modern and appropriate design issues. Cloud computing offers many possibilities for high performance computing.
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
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.
