[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-127262-en":3,"doc-seo-127262-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},127262,2336475104362,"Eden","https://ap-avatar.wpscdn.com/avatar/22000c4c46a41b752dd?x-image-process=image/resize,m_fixed,w_180,h_180&k=1786595829695023868",8,"Research & Report","Science platform for machine learning of big astronomical data - data analysis modules","The thesis implements analysis modules enabling selected machine learning methods to be applied to astronomical spectra within an emerging cloud-based science platform. The delivered components include preprocessing, active deep learning, and dimensionality reduction modules, as well as a front-end part for platform access. All modules can be executed from the terminal, while preprocessing and active deep learning can also be launched through a web interface. The platform supports exploratory discovery of interesting and unusual spectra.","SCIENCE PLATFORM FOR MACHINE LEARNING OF BIG ASTRONOMICAL DATA – DATA ANALYSIS MODULES  \nAlisher Laiyk  \nBachelor’s thesis  \nFaculty of Information Technology Czech Technical University in Prague Department of Software Engineering Study program: Informatics  \nSpecialisation: Software engineering  \nSupervisor: RNDr. Petr. Škoda, CSc.  \nMay 16, 2025  \nTitle:  \nAssignment of bachelor’s thesis  \nScience platform for machine learning of big astronomical data-data analysis modules  \nStudent: Alisher Laiyk  \nSupervisor: RNDr. Petr Škoda, CSc.  \nStudy program: Informatics  \nBranch / specialization: Software Engineering 2021  \nDepartment: Validity:  \nDepartment of Software Engineering  \nuntil the end of summer semester 2025/2026  \nInstructions  \nScience Platform is an emerging cloud-based technology for processing and exploratory analysis of big data sets in astronomy and earth sciences. The goal of the thesis is the design, implementation, testing and integration of several data analysis modules needed for performing selected machine learning methods on a large volume of astronomical spectra (namely active deep learning and dimensionality reduction as e.g. tSNE, PCA, UMAP) including various methods of preprocessing and data visualization.  \nThis thesis is complemented by the thesis of Olexandr Burakov focused on development of a cloud infrastructure allowing to launch these modules according to simple workflow manager through custom API.  \nThe key tasks are:  \n1) Analyse data and parameters requirements of the typical machine learning procedures applied on spectra, mainly the active deep learning and tSNE.  \n2) Identify the best libraries for performing selected algorithms of pre-processing, active deep learning and dimensionality reduction as well as solutions for Big Data  \nvisual analysis.  \n3) Wrap such data analysis modules by the API defined in thesis of O. Burakov.  \n4) Prepare simple workflow scripts allowing to run the modules through the above mentioned API.  \n5) Demonstrate the correct functionality of workflows on a suitable data sets (e.g. LAMOST or SDSS spectra).  \nElectronically approved by Ing. Michal Valenta, Ph.D. on 8 November 2024 in Prague.  \n6) Integrate the modules with the help of O. Burakov into his cloud infrastructure  \n7) Discuss the performance and flexibility of your solution and suggest future improvements and extensions towards much larger and different type of astronomical data sets (e.g. light curves, images).  \nRecommended literature and suggested tools and libraries will be provided by supervisor.  \nElectronically approved by Ing. Michal Valenta, Ph.D. on 8 November 2024 in Prague.  \nCzech Technical University in Prague Faculty of Information Technology © 2025 Alisher Laiyk. All rights reserved.  \nThis thesis is school work as defined by Copyright Act of the Czech Republic. It has been submitted at Czech Technical University in Prague, Faculty of Information Technology. The thesis is protected by the Copyright Act and its usage without author’s permission is prohibited (with exceptions defined by the Copyright Act) .  \nCitation of this thesis: Laiyk Alisher. Science platform for machine learning of big astronomical data – data analysis modules. Bachelor’s thesis. Czech Technical University in Prague, Faculty of Information Technology, 2025 .  \nI would like to thank my supervisor RNDr. Petr Škoda CSc.for his help and advice in writing this thesis. My deep gratitude also goes to my family and friends, who supported me during my studies.  \niv  \nDeclaration  \nI hereby declare that the presented thesis is my own work and that I have cited all sources of information in accordance with the Guideline for adhering to ethical principles when elaborating an academic final thesis.  \nI acknowledge that my thesis is subject to the rights and obligations stipulated by the Act No. 121/2000 Coll., the Copyright Act, as amended. In accordance with Section 2373(2) of Act No. 89/2012 Coll., the Civil Code, as amended, I her","cbCaihRwz8MQgMXQ","https://ap.wps.com/l/cbCaihRwz8MQgMXQ","pdf",859372,1,49,"English","en",105,"# Instructions\n## Data analysis modules for machine learning on spectra\n## Key tasks\n## Integration and evaluation\n## Thesis declaration and ethical principles","[{\"question\":\"What is the main goal of the thesis?\",\"answer\":\"To design, implement, test, and integrate data analysis modules for applying selected machine learning methods to large volumes of astronomical spectra on a cloud platform.\"},{\"question\":\"Which machine learning methods and analysis steps are covered?\",\"answer\":\"The thesis focuses on preprocessing, active deep learning, and dimensionality reduction, including methods such as tSNE, PCA, and UMAP, along with data visualization for big data.\"},{\"question\":\"How can the implemented modules be run?\",\"answer\":\"Modules can be run from the terminal, and preprocessing and active deep learning modules can additionally be launched through a web interface.\"}]","Science platform for machine learning of big astronomical data - 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