The module is devoted to intelligent computer technologies and includes the following sections: Genetic algorithms for symbolic information processing tasks; Genetic algotirhms for constrained and unconstrained optimization tasks; Knowledge-based systems; Knowledge-based agents; Pattern recognition and image processing in a fuzzy environment; Intelligent systems as the basis of probabilistic reasoning. Learning systems in fuzzy and probabilistic environments; Hybrid intelligent technologies.

Message Passing Interface (MPI) is a standardized and portable message-passing standard designed to function on parallel computing architectures.

Introduction to methods for identifying valid, novel, useful, and understandable patterns in data. Data preprocessing Induction of predictive models from data: classification, regression, probability estimation. Discovery of clusters and association rules.

Our goal is to demystify AI. The Elements of AI is a series of free online courses created by Reaktor and the University of Helsinki. We want to encourage as broad a group of people as possible to learn what AI is, what can (and can’t) be done with AI, and how to start creating AI methods. The courses combine theory with practical exercises and can be completed at your own pace.

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 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.

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.

The study course consists from the following topics:
-introduction to computer program process parallelization and cloud technology usage.
-CPU several core usage in one calculation process.
-cloud technology usage opportunities.
-practical classes connected to CPU several core usage into one calculation process.
-practical classes connected to cloud technology usage in computer program.

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

The current course provides knowledge on Cloud Computing (CC) architecture, design and maintenance of CC systems, and cloud services. During the course, the students will learn how to setup and administrate IaaS (Infrastructure-as-a-Service) system. Objectives: to make understanding about Cloud Computing Architecture, its systems structure and service and development models, as well as impart knowledge about Cloud Computing use benefits and problems.

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.

This course is focused on the practical aspects of Machine Learning. Within the course students get familiar with with the techniques of preprocessing and visualization for data analysis. Study course provide a review of the most common algorithms for supervised and unsupervised learning, as well as an introduction to Deep Learning.

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.

As the power and capabilities of computing increases, Artificial Intelligence solutions takes a greater role to perform and execute various processes. Being a part of Artificial Intelligence, Machine Learning provides computer learning and decision-making based on the provided data. Seminar is intended to provide insight into Machine Learning and its algorithms covering supervised and unsupervised learning, including data processing and application for machine learning solutions.

The students will understand and be able to apply their knowledge about intelligent software algorithms.

The goal of the course is to introduce the students to the basics of data analysis and machine learning methods as an additional tool for finding patterns in data and issuing predictions, by working with data from various physical systems.

The tasks of the course are to introduce the students to the elements of data analysis - cleaning, analysis and visualization, based on data from real physical systems; to apply the machine learning algorithms by mathematically modelling various physical systems.

This study course covers the theoretical and practical principles of high performance computing that are implemented using graphics processing hardware and specialized software. The study course includes an overview of CUDA parallel computing platform architecture based on graphics processors, parallel computing algorithms, application libraries and tools. An in-depth focus is put on the interdisciplinary application of CUDA, for example, in the areas of big data analysis, interoperability with computer graphics, image processing, computational modelling and machine learning.

As a result of this study course the student acquires basic knowledge about multiphysical modeling, basic steps of problem-solving, verification and analysis of results. The acquired competencies allow to  hoose a physical model suitable for the description of the physical  process, to explain physical processes on the basis of obtained results and to give recommendations for optimization of the physical process.

After the completion of this course, the students will have the knowledge of the architectures with high performance. They will understand the systems that are used for high performance computing and they will have the knowledge for algorithm speedup by their analysis and transformation based on available hardware infrastructure especially on their processor and memory hierarchy. 

Architecture and elements of artificial neural networks. Perceptron. Adaptation algorithms. Development of NN technology. Learning methods for single-layer and multi-layer perceptrons. Optimization and forecasting problems. Software. Neurocomputing: algorithms and applications. Design of artificial neural systems: commercial products. Application of artificial neural networks. Cluster analysis. Classification.