Study Modules

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Algorithms in Bioinformatics

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.

Implementing the FAIR Data Principles in Research

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.

High-Performance Computing in Physics

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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.

Learning with structured data

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.

Cybersecurity Solutions in High Performance Computing Environment

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The study course covers the theoretical and practical principles of high-performance computing in the context of cybersecurity by using graphics processing hardware and dedicated software. The study course includes an overview of architecture, computing algorithms, software libraries and tools of parallel computing platform CUDA based on graphics processors. An in-depth attention is devoted to the cross-disciplinary application of CUDA in the areas of vulnerability analysis, parallel data encryption, surveillance data mining, object detection and recognition.

Mathematical and statistical software packages

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The course introduces students to the basic principles that should be acquired to mathematicians, statisticians, economists and financiers and others in order to use software packages R, SPSS and MATLAB for their scientific research and practical work. An overview is given about the appropriate application features and options of the software packages R, SPSS and MATLAB focusing primarily on the students’ needs to work out different practice, bachelor and other scientific works.

Artificial neuron and neural networks

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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.

Deep Learning

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Machine learning is the process of developing and applying predictive algorithms to predicting future outcomes using available training data. Machine learning is a corner-stone for Data science, Big Data analytics, Robotics, Natural language processing and AI in general. Course introduces students to supervised and unsupervised machine learning concepts and algorithms from basic classification to Deep Learning with artificial neural networks. As computers and GPUs become more powerful, Deep Neural Networks are gradually taking over from simpler Machine Learning methods.

Intelligent computer technologies and systems

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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.

Elements of AI

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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.

Artificial neuron and neural networks

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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.