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| − | {{#seo: |title=Research management course|titlemode=append|keywords=Research management course|description=This research management course immerses students in research activities that produce scientific papers}}
| + | #REDIRECT [[My first scientific paper]] |
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| − | [[File:Miai logo1.jpeg|class=img-responsive|left|alt=Research management course|link=Course_schedule]]
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| − | {{Box|Title=News and announcements|Content={{News}}<!--''[[News|more]]''-->}}
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| − | == m1p Week 1 ==
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| − | * [https://www.youtube.com/live/c3eiTdWVepo Video]
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| − | * [[Week 1|Home work]]
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| − | == m1p Course progress==
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| − | This course produces student research papers. It gathers research teams. Each team joins a student, a consultant, and an expert. The student is a project driver who wants to plunge into scientific research activities. The graduate student consultant conducts the research and helps the student. The expert, a professor, states the problem and enlightens the road to the goal. The projects start in February and end in May, according to the [[Course schedule|schedule]].
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| − | * [https://github.com/intsystems/m1p/tree/main-2025 List of problems for 2025 is in progress until March 20th.]
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| − | *[[Course schedule]], Spring 2025
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| − | *[[Week 0|Week 0: Come in]]
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| − | *[[Week 1|Week 1: Set the toolbox]]
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| − | <!-- ** [https://forms.gle/FEJ28KEjxdj6Zgha7 Questionnaire 1 - '''Imagine your project'''] -->
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| − | *[[Week 2|Week 2: Select your project and tell about it]]
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| − | <!--** [https://forms.gle/tmGNe6pbVHtr4cZb6 Questionnaire 2 - '''Check your terminology''']-->
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| − | *[[Week 3|Week 3: State your problem]]
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| − | *[[Week 4|Week 4: Plan the experiment]]
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| − | *[[Week 5|Week 5: Visualise the principle]]
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| − | *[[Week 6|Week 6: Write the theory]]
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| − | *[[Week 7|Week 7: Analyse the error]]
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| − | *[[Week 8|Week 8: Construct your paper]]
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| − | *[[Week 9|Week 9: Review a paper]]
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| − | *[[Week 10|Week 10: Select a journal to submit]]
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| − | *[[Week 11|Week 11: Prepare your presentation]]
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| − | *[http://www.youtube.com/watch?v=xW_lXGn1WHs Week 12: Show your results (Youtube)]
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| − | <b>LINKS</b>
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| − | *2024 results [https://github.com/intsystems/m1p/tree/main-2024 GitHub]
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| − | *2024 problems [https://github.com/intsystems/m1p/blob/main-2024/problem_list.md GitHub]
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| − | *2023 problems [https://github.com/intsystems/m1p/blob/main-2023/problem_list.md GitHub]
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| − | *2022 results [https://github.com/Intelligent-Systems-Phystech/m1p_2022 GitHub]
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| − | *Telegram: [https://t.me/m1p_org discussion] <b> Ask here! </b>
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| − | *The meeting room: [https://m1p.org/go_zoom m1p.org/go_zoom]
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| − | *More courses from the [http://m1p.org/is Intelligent Systems]
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| − | <!--
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| − | *Check-1 and check-2 sing-in: [https://docs.google.com/spreadsheets/d/1g9ud_qyHJIkzHYWFRac_WmeVZ7Qv11N_OIXAzK5iOs0/edit#gid=298225191 table]
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| − | *Stepik: [https://stepik.org/course/90240/syllabus peer-review and quiz]
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| − | *Machinelearning: [http://www.machinelearning.ru/wiki/index.php?title=%D0%9C%D0%BE%D1%8F_%D0%BF%D0%B5%D1%80%D0%B2%D0%B0%D1%8F_%D0%BD%D0%B0%D1%83%D1%87%D0%BD%D0%B0%D1%8F_%D1%81%D1%82%D0%B0%D1%82%D1%8C%D1%8F_%28%D0%BB%D0%B5%D0%BA%D1%86%D0%B8%D0%B8_%D0%B8_%D0%BF%D1%80%D0%B0%D0%BA%D1%82%D0%B8%D0%BA%D0%B0%2C_%D0%92.%D0%92._%D0%A1%D1%82%D1%80%D0%B8%D0%B6%D0%BE%D0%B2%29/%D0%93%D1%80%D1%83%D0%BF%D0%BF%D1%8B_874%2C_821%2C_813%2C_%D0%B2%D0%B5%D1%81%D0%BD%D0%B0_2021 group table]
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| − | -->
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| − | | |
| − | <!--
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| − | <b>CONTENTS</b>
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| − | [[Fundamental theorem]]|[[Todo list]]|[[Books]]|[[Reviews]]|[[Tools]]|[[Projects]]|[[Proposals]]|[[Templates]]|[[Career]]|[[Notation]]|[[Publication]]
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| − | -->
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| − | <b>HISTORY</b>
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| − | * [http://www.machinelearning.ru/wiki/index.php?title=M1 Main page with old homework]
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| − | * [http://bit.ly/m1p_2020 Group 674, 694, spring 2020]
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| − | * [http://bit.ly/M1_2019_674 Group 674, spring 2019]
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| − | * [http://bit.ly/M1_2019_694 Group 694, spring 2019]
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| − | == The Art of Scientific Research ==
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| − | '''See you this [https://m1p.org/go_zoom Saturday at 11:10 m1p.org/go_zoom]'''
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| − | The goal is to select and prepare the research topic of your dream. We must be sure that the problem statement and project planning lead you to successful delivery.
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| − | * [[The Art of Scientific Research|The course syllabus]]
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| − | ** [https://www.youtube.com/watch?v=Vz67fVTQoaE&list=PLk4h7dmY2eYEA8lKRpk5Fy5yLyGqdED9I Youtube playlist]
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| − | ** [https://github.com/vadim-vic/the-Art-homework Repository template]
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| − | * [[Step 0|Step 0: We start]]
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| − | * [[Step 1|Step 1: Highlight your work]]
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| − | * [[Step 2|Step 2: Describe an industrial project]]
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| − | * [[Step 3|Step 3: Explain the method]]
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| − | * [[Step 4|Step 4: Graphical highlights]]
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| − | * [[Step 5|Step 5: Deliver your message: slides 2 and 3]]
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| − | * [[Step 6|Step 6: Risk management in research planning]]
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| − | * [[Step 7|Step 7: Yield foundation of your research]]
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| − | * [[Step 8|Step 8: Descriptive tools for your problem]]
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| − | * [[Step 9|Step 9: Launch your project with foundation and statement]]
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| − | * [[Step 10|Step 10: Computational experiment and visualizing]]
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| − | * [[Step 11|Step 11: The final talk]]
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| − | ==Mathematical forecasting, 2024==
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| − | This course delivers methods of model selection in machine learning and forecasting. The modeling data are videos, audio, encephalograms, fMRIs, and other measurements in natural science. The models are linear, tensor, deep neural networks, and neural ODEs. The practical ''examples'' are brain-computer interfaces, weather forecasting, and various spatial-time series forecasting. The ''lab works'' are organized as paper-with-code reports. [[Mathematical forecasting|See the course page]]
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| − | ==Functional Data Analysis, 2024==
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| − | The statistical analysis of spatial time series requires additional methods of data analysis. First, we suppose time is continuous, put to the state space changes <math>\frac{d\mathbf{x}}{dt}</math> and use neural ordinary and stochastic differential equations. Second, we analyze a multivariate and multidimensional time series and use the tensor representation and tensor analysis. Third, since the time series have significant cross-correlation we model them in the Riemannian space. Fourth, medical time series are periodic, the base model is the pendulum model, <math>\frac{d^2x}{dt^2}=-c\sin{x}</math>. We use physics-informed neural networks to approximate data. Fifth, the practical experiments involve multiple data sources. We use canonical correlation analysis with latent state space. This space aligns the source and target spaces and generates data in source and target manifolds. [[Functional Data Analysis|See the course page]].
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| − | <!--*[http://bit.ly/m1p_file2discuss Upload a file to discussion]
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| − | *All questions to <strong>mlalgorithms [at] gmail [dot] com,</strong>
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| − | <strong>MediaWiki has been installed.</strong>
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| − | Consult the [https://www.mediawiki.org/wiki/Special:MyLanguage/Help:Contents User's Guide] for information on using the wiki software.
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| − | = Getting started =
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| − | * [https://www.mediawiki.org/wiki/Special:MyLanguage/Manual:Configuration_settings Configuration settings list]
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| − | * [https://www.mediawiki.org/wiki/Special:MyLanguage/Manual:FAQ MediaWiki FAQ]
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| − | * [https://lists.wikimedia.org/mailman/listinfo/mediawiki-announce MediaWiki release mailing list]
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| − | * [https://www.mediawiki.org/wiki/Special:MyLanguage/Localisation#Translation_resources Localise MediaWiki for your language]
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| − | * [https://www.mediawiki.org/wiki/Special:MyLanguage/Manual:Combating_spam Learn how to combat spam on your wiki]
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| − | <!--
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| − | [[File:bottom.jpeg|class=img-responsive|center|alt=Research management course|link=Course_schedule]]
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