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.

The aim of the course is to create an insight into high-performance computing in Physics. The tasks of the course are: (1) to overview applications of parallel algorithms in Physics problems, (2) to overview methods of parallel computing, (3) to learn how to use high-performance libraries, (4) to analyse efficiency of parallel algorithms, (5) to gain an experience in using supercomputing centres.

As the power and capabilities of computing increases, Artificial Intelligence solutions takes a greater role to perform and execute various processes. Seminar is intended to provide insight into artificial neural networks, give practical examples of deep learning applications and solution implementation using Python and Tensorflow.

Participants will get hands-on experience in implementing deep learning solutions by using Python which currently is one of the most popular programming languages.

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.

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.

Student gets introduced in most important algorithmic methods used within the field. For the problems considered, algorithms for their solution are studied and analyzed, several of these algorithms students have to implement in a programming language of their choice. Course emphasizes bioinformatics problems that are most important with respect to practical applications - protein and nucleotide sequence and protein structure analysis, although a brief introduction in other subfields of bioinformatics is given. Course also gives a brief introduction in main bioinformatics databases.

During the course, students will learn basics about the machine learning techniques and the neural networks, researching the image classification and object detection problems. Students will learn how to work with the machine learning framework Tensorflow, develop new machine learning models as well asuse existing models. Students will learn the cloud platform for model running and learning. During the practical assignments, students will develop software for object detection on the images.

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 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 current course provides knowledge about server virtualization. The students will learn how to setup the server virtualization and how to configure the necessary hardware and virtual machines for development and maintenance of server infrastructure and for computer system administration. The students will work with several operating systems to install and configure the main internet services, including web services, e-mail, ssh, ftp, nfs, etc.