[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-117911-en":3,"doc-seo-117911-105":30,"detail-sidebar-cat-0-en-105":92},{"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":20,"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},117911,8796095360427,"Lucas Martin","https://ap-avatar.wpscdn.com/davatar_994ba38a5ba835b3df7d355c54d3ed8d",8,"Research & Report","APPLYING MACHINE LEARNING - A MULTI-ROLE PERSPECTIVE - Doctoral Dissertation","Machine (and deep) learning technologies increasingly influence multiple application domains, from web search and social-network content filtering to e-commerce recommendations and mobile apps, supported by real-time information collection through devices and internet sites. Their pervasive adoption generates huge data volumes that strain conventional platforms and accelerate the use of machine and deep learning. This dissertation treats machine learning as a way of thinking, examining tasks from varied perspectives—non-data-scientist workflows, end-to-end problem setup, effective classification implementations, and user experience enhancement via data visualization and eXtended Reality.","Alma Mater Studiorum-Università di Bologna  \nDOTTORATO DI RICERCA IN  \nDATA SCIENCE AND COMPUTATION  \nCiclo 34  \nSettore Concorsuale: 01/B1-INFORMATICA  \nSettore Scientifico Disciplinare: INF/01-INFORMATICA  \nAPPLYING MACHINE LEARNING: A MULTI-ROLE PERSPECTIVE  \nPresentata da: Alessia Angeli  \nCoordinatore Dottorato Supervisore  \nProf. Daniele Bonacorsi Prof. Gustavo Marfia  \nCo-supervisore  \nProf. Marco Roccetti  \nEsame finale anno 2023  \nDeclaration  \nI declare that, except where stated otherwise by specific reference to the work of others, this dissertation has been composed solely by myself and it has not been submitted, in whole or in part, in any previous application for any other degree or any other university. The work presented is entirely my own except where specified, in the text and Acknowledgements, regarding research collaborations with others. The collaborative contributions have been indicated clearly and due references have been provided on all supporting literature and resources.  \nParts of this work have been published in [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13], or submitted in [14] .  \nJanuary 2023  \nii  \nAbstract  \nMachine (and deep) learning technologies are more and more present in several fields. It is undeniable that many aspects of our society are empowered by such technologies: web searches, content filtering on social networks, recommendations on e-commerce websites, mobile applications, etc. , in addition to academic research. Moreover, mobile devices and internet sites, e.g., social networks, support the collection and sharing of information in real time. The pervasive deployment of the aforementioned technological instruments, both hardware and software, has led to the production of huge amounts of data. Such data has become more and more unmanageable, posing challenges to conventional computing platforms, and paving the way to the development and widespread use of the machine and deep learning. Nevertheless, machine learning is not only a technology. Given a task, machine learning is a way of proceeding (a way of thinking), and as such can be approached from different perspectives (points of view) . This, in particular, will be the focus of this research. The entire work concentrates on machine learning, starting from different sources of data, e.g. , signals and images, applied to different domains, e.g., Sport Science and Social History, and analyzed from different perspectives: from a non-data scientist point of view through tools and platforms; setting a problem stage from scratch; implementing an effective application for classification tasks; improving user interface experience through Data Visualization and eXtended Reality. In essence, not only in a quantitative task, not only ina scientific environment, and not only from a data-scientist perspective, machine (and deep) learning can do the difference.  \niv  \nContents  \n1 Introduction 1  \n2 Machine learning from a non-data scientist point of view 5  \n2.1 Introduction ............................... 5  \n2.2 Related works .............................. 8  \n2.2.1 Data-human interfaces ..................... 8  \n2.2.2 Human activity recognition .................. 9  \n2.3 Contributions .............................. 11  \n2.4 Dataset ................................. 11  \n2.5 Methods and experiments ....................... 13  \n2.5.1 Weka ............................... 15  \n2.5.2 Orange .............................. 18  \n2.5.3 Ludwig .............................. 20  \n2.5.4 Knime .............................. 23  \n2.5.5 Python .............................. 26  \n2.6 Discussion and conclusions ....................... 30  \n3 Setting the stage for a machine learning task 35  \n3.1 Introduction ............................... 35  \n3.2 Related works .............................. 39  \n3.3 Contributions .............................. 43  \n3.4 Experimental setup and procedure .................. 44  \n3.4.1 Recruitment ........................... ","cbCaivkDwYagW7i1","https://ap.wps.com/l/cbCaivkDwYagW7i1","pdf",64749119,1,258,"English","en",105,"# Introduction\n# Machine learning from a non-data scientist point of view\n## Related works\n## Contributions\n## Dataset\n## Methods and experiments\n## Discussion and conclusions\n# Setting the stage for a machine learning task\n## Related works\n## Contributions\n## Experimental setup and procedure\n## Dataset\n## Method\n## Results\n## Discussion\n## Conclusions\n## Future works\n# A detailed machine learning application\n## Related works\n## Contributions\n## Dataset\n## Socio-historical background\n## Idea of cataloging tool","[{\"question\":\"What perspectives does the dissertation emphasize for machine learning?\",\"answer\":\"It focuses on multiple points of view, including a non-data-scientist approach using tools and platforms, setting up tasks from scratch, implementing classification applications, and improving user experience through data visualization and eXtended Reality.\"},{\"question\":\"Why does the dissertation motivate machine learning beyond conventional computing?\",\"answer\":\"It explains that widespread deployment of digital technologies produces massive datasets that become difficult to manage with conventional computing platforms, enabling broader development and adoption of machine and deep learning.\"},{\"question\":\"What kinds of data and domains are investigated?\",\"answer\":\"The work considers different data sources such as signals and images, applied to domains including Sport Science and Social History, and analyzed through the different perspectives described above.\"}]","APPLYING MACHINE LEARNING - 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