удк 364.2:331 tutoVA o.V., phd econ., Senior research associate, international research and training center for information technologies and Systems, of the NaS and of the meS of Ukraine, acad. glushkov ave., 40, kyiv, 03187, olenatutova@gmail.com sAVChenKo ye.A., phd eng., Senior researcher associate, international research and training center for information technologies and Systems, of the NaS and of the meS of Ukraine, acad. glushkov ave., 40, kyiv, 03187, savchenko_e@meta.ua Development Index over 2002. Also modeling of the indicators characterising the development of information and telecommunication technologies in Ukraine will be done. uKrAIne In the InFormAtIon AnD CommunICAtIon teChnoloGy DeVelopment rAnKInG. I This article considers the level and evolution of information and communication technology developments in Ukraine and its experience relative to other Eastern European and post-Soviet countries based on the Information and Communication Technology

The concepts of metalearning as one of the tasks of machine learning are considered. The basic principles of metalearning g and examples of solving problems of machine and metalearning in various fields of human activity are given. It is planned for a decision support system construction based on an inductive approach for complex processes modeling and forecasting.

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The paper considers the level and evolution of information and communication technology developments in Ukraine and its experience relative to other Eastern European and post-Soviet countries based on the Information and Communication Technology Development Index over 2002-2017. Also modeling of the indicators characterising the development of information and telecommunication technologies in Ukraine is presented. The GMDH models are built to forecast when the percentage of individuals using the Internet in Ukraine will achieve the same level as Kazakhstan and Belarus. This models shown that Ukraine will be on the point of overtaking Belarus in the end of the forecasted period but won't reach the level of Kazakhstan because of much more extensive development of ICTs there.

Introduction. Sailing is a sport that places high demands on the physical fitness of an athlete. Despite the fact that this sport has been practiced for a very long time, experts have not paid due attention to assessing the impact of the athlete’s load during training on his physical fatigue. The use of mathematical modeling methods at the stage of training athletes to select the optimal load will make training more effective. In order to determine the modeling method, which will allow quite simply and easily to find the dependence of the optimal physical load on the physiological indicators of athletes, a review of the most popular modeling methods in sports was carried out. The purpose of this article is to study the problem of modeling the optimal training load in sailing in order to find an effective method for building models of the dependence of the athlete’s fatigue indicators on the indicators characterizing the athlete’s condition. Results. A number of indicators characterizing the choice of optimal physical activity in sailing are given. An approach to the construction of a mathematical model for choosing the optimal physical load for yachtsmen during training is proposed. Conclusions. The article explores approaches to modeling the processes that occur during the selection of a training load in sports. Based on a review of existing methods, an inductive approach was chosen to build models for choosing the optimal load in sailing. It is planned that with the help of this approach, models of the dependence of indicators characterizing physical activity on indicators characterizing the state of athletes will be obtained, which will be given in subsequent works.