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Computer Science in Sport

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Produktnummer: 18f1ceb79012394eb99e30a628a2bd036f
Themengebiete: Python artifical intelligence (AI) big data deep learning machine learning (ML) perofmance indicators physiological modelling position data, event data, video data, text data predictive models programming languages
Veröffentlichungsdatum: 06.03.2024
EAN: 9783662683125
Sprache: Englisch
Seitenzahl: 249
Produktart: Kassette / Medienmix z.B. Audio und Buch
Herausgeber: Memmert, Daniel
Verlag: Springer Berlin
Untertitel: Modeling, Simulation, Data Analysis and Visualization of Sports-Related Data
Produktinformationen "Computer Science in Sport"
In recent years, computer science in sport has grown extremely, mainly because more and more new data has become available. Computer science tools in sports, whether used for opponent preparation, competition, or scientific analysis, have become indispensable across various levels of expertise nowadays. A completely new market has emerged through the utilization of these tools in the four major fields of application: clubs and associations, business, science, and the media. This market is progressively gaining importance within university research and educational activities.This textbook aims to live up to the now broad diversity of computer science in sport by having more than 30 authors report from their special field and concisely summarise the latest findings. The book is divided into four main sections: data sets, modelling, simulation and data analysis. In addition to background information on programming languages and visualisation, the textbook is framed by history and an outlook. Students with a connection to sports science are given a comprehensive insight into computer science in sport, supported by a didactically sophisticated concept that makes it easy to convey the learning content. Numerous questions for self-testing underpin the learning effect and ensure optimal exam preparation. For advanced students, the in-depth discussion of time series data mining, artificial neural networks, convolution kernels, transfer learning and random forests offers additional value.
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