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.
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.
Data management and mining supply methods and technologies can be used to transform available data into the useful information and knowledge. Data management solves the data retrieval, transformation and structuring tasks. As a result the data is prepared for the analysis. Data mining solves the data analysis tasks, giving ability to find yet unknown relationships in data. Data mining results let enterprises take correct and wise decisions.
The course on the fundamentals of ontology in data mining is intended to provide information technology students with an insight into knowledge structures and their possible applications. The aim of the course is to inform about the approaches and tools that allow the acquisition, description, structuring and use of formally described and structured knowledge. It offers the opportunity to acquire the skills necessary for the creation and application of a knowledge base.
