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{{#seo:
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|title=Research management course
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|description=This research management course immerses students in research activities that produce scientific papers.
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[[File:Miai_logo1.jpeg|class=img-responsive|left|alt Maths&AI MIPT-UGA student workshop]]  
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{{Box|Title=News and announcements|Content={{News}}<!--''[[News|more]]''-->}}
  
=== June 11th, Thursday 14:30===
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===Course progress===
* Seminar: ''Comprehensive problem of human behavioral analysis''
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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]].
# [https://us02web.zoom.us/j/82424101188 Zoom conference]
 
<!--# [https://docs.google.com/document/d/1bA1YbSiM5cHcrSQYexyQm4IyIzNCBqGXcGMAH-Nb278/edit?usp=sharing '''Plan of the talk''']-->
 
* May 14th, Thursday 14:30 Seminar: ''Plans of future research''
 
* May 7th, Thursday 14:30 Seminar: ''End of the m1p course: results and discussion''
 
  
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*[[Course schedule]]
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*[[Week 0|Week 0: Come in]]
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** [https://forms.gle/hFiu8j3fHF9hdZkN8 Questionnaire 0]
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** [https://docs.google.com/document/d/1cESrKzU_kTL1PeRNhkQ1glzjbGh3VLw7Cbt34O19NGQ/edit?usp=sharing Scenario]
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*[[Week 1|Week 1: Set the toolbox]]
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*[[Week 2|Week 2: Select your project and tell about it]]
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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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===Links===
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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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*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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=== Next months: May 7th - August 21rd ===
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<!--  
# Prepare your projects to publish and promotion.
 
# Submit your thesisis to conferences.
 
# Show your results to get
 
#* scholarships,
 
#* internships,
 
#* [https://postnauka.ru/lists/98446 grants].
 
 
 
 
==Contents==
 
==Contents==
*[[Course schedule]]
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[[Fundamental theorem]]|[[Todo list]]|[[Books]]|[[Reviews]]|[[Tools]]|[[Projects]]|[[Proposals]]|[[Templates]]|[[Career]]|[[Notation]]|[[Publication]]
*[[Todo list]]
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*[http://mlalgorithms.fun/ Course progress]
 
*[https://t.me/m1p_qs Telegram: Questions to Machine learning]
 
<!--*[https://t.me/m1p_news Announcements and news (does not work yet) ]-->
 
*[http://bit.ly/m1p_file2discuss Upload a file to discussion]
 
* New pages to fill
 
** [[Books]]
 
** [[Reviews]]
 
** [[Tools]]
 
** [[Projects]]
 
** [[Templates]]
 
** [[Career]]
 
** [[Notation]]
 
  
==Basic materials==
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===History===
 
* [http://www.machinelearning.ru/wiki/index.php?title=M1 Main page with old homework]
 
* [http://www.machinelearning.ru/wiki/index.php?title=M1 Main page with old homework]
 
* [http://bit.ly/m1p_2020  Group 674, 694, spring 2020]
 
* [http://bit.ly/m1p_2020  Group 674, 694, spring 2020]
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* [http://bit.ly/M1_2019_694 Group 694, spring 2019]
 
* [http://bit.ly/M1_2019_694 Group 694, spring 2019]
  
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==Mathematical methods of forecasting, 2024==
  
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This course delivers methods of model selection in machine learning and forecasting. The modeling data are videos, audios, 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 course page]]
  
  
All questions to <strong>mlalgorithms [at] gmail [dot] com,</strong>
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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>
See you on the course,
 
 
 
[http://strijov.com/papers_ru.html V. V. Strijov]
 
 
 
  
  
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Latest revision as of 03:37, 7 February 2024

alt Maths&AI MIPT-UGA student workshop

 

News and announcements

Spring 2024 in January — My fist scientific paper: Suggest your project here!

Spring 2024 in February 8th, 16:10 — My fist scientific paper starts

Fall 2023 Wednesday — Mathematical metods of forecasting

Each Thursday at 17:40 — the class My fist scientific paper m1p.org/go_zoom

Course progress

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 schedule.

Links


History

Mathematical methods of forecasting, 2024

This course delivers methods of model selection in machine learning and forecasting. The modeling data are videos, audios, 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. See course page