Research generates significant amounts of data that are used to communicate the results of a particular investigation. However, currently, these data are usually unstructured and highly scattered. As a result, this data can not be used to verify, replicate or reanalyze the findings. Moreover, different standards, annotation practices and data formats for data and metadata (if available) might have been used by other researchers, introducing additional heterogeneity and difficulties in later data integration and interpretation.

The traditional approach to research programmes is to assume that students will find a way to analyse and visualise their data. This assumption brings problems for the students, their supervisors and a significant waste of time. Many students are scared by the data rather than curious and usually skip exploratory data analysis and go straight to advanced statistical models that they cannot explain later because they do not understand their data in depth.

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.