Difference between revisions of "My first scientific paper"

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=== m1p Week 7 ===
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==Foundation AI Models for Science, 2025==  
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'''Foundation AI models''' are universal models to solve a wide set of problems. This project proposes to investigate the theoretical properties of foundation models. The domain to model is a spatial-time series. These data are used in various scientific disciplines and serve to generalise scientific knowledge and make forecasts. The essential problems, formulated as user requests that solve a foundation model, are forecasting and generation of time series; analysis and classification of time series; detection of change point, and causal inference. To solve these problems, the foundation AI models are trained on massive datasets. The main goal of this project is to compare various architectures of foundation models to find an optimal architecture that solves the listed problems for a wide range of spatial time series.
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[[Functional Data Analysis|See the FDA page]].
  
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* [Video]
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==Mathematical forecasting, 2025==
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* [[Week 8|Homework]]
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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 page]]
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* [https://forms.gle/N24xtfmjUScirUVHA Questionnaire]
 
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<!--[https://forms.gle/SLAs8nmZZaseQEw17 Questionnaire]-->
 
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<!--* [https://github.com/intsystems/m1p/tree/main-2025 Slides to discuss]-->
 
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<!--https://t.me/+U2BboF1JcfFhNTUy Questionnaire] -->
 
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<!--https://forms.gle/V5pjhYzBM3fJCHpu9 Qs-->
 
  
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== m1p Course progress==
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== My first scientific paper ==
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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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This course produces student research papers. It gathers research teams. Each team consists of 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 way 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 The list of problems for 2025]
 
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*[[Course schedule]]: Spring 2025
 
 
*[[Week 0|Week 0: Sign up]]
 
*[[Week 0|Week 0: Sign up]]
 
*[[Week 1|Week 1: Set the toolbox]]
 
*[[Week 1|Week 1: Set the toolbox]]
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*[[Week 10|Week 10: Select a journal to submit]]
 
*[[Week 10|Week 10: Select a journal to submit]]
 
*[[Week 11|Week 11: Prepare your presentation]]
 
*[[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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*[https://www.youtube.com/watch?v=uwcbMJamBbM Week 12: Show your results (Youtube)]
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<!-- [http://www.youtube.com/watch?v=xW_lXGn1WHs Week 12: Show your results (Youtube)] -->
  
 
===Links===
 
===Links===
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*2025 results [https://github.com/intsystems/m1p/tree/main-2025 GitHub]
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* 2025 results [https://github.com/intsystems/m1p/tree/main-2025 GitHub]
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*2024 results [https://github.com/intsystems/m1p/tree/main-2024 GitHub]
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* 2025 [https://github.com/intsystems/m1p/tree/main-2025 The list of problems for 2025]
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*2024 problems [https://github.com/intsystems/m1p/blob/main-2024/problem_list.md  GitHub]
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* 2024 results [https://github.com/intsystems/m1p/tree/main-2024 GitHub]
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*2023 problems [https://github.com/intsystems/m1p/blob/main-2023/problem_list.md  GitHub]
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* 2024 problems [https://github.com/intsystems/m1p/blob/main-2024/problem_list.md  GitHub]
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*2022 results [https://github.com/Intelligent-Systems-Phystech/m1p_2022 GitHub]
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* 2023 problems [https://github.com/intsystems/m1p/blob/main-2023/problem_list.md  GitHub]
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<!-- *Telegram: [https://t.me/m1p_org discussion] <b> Ask here! </b> -->
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* 2022 results [https://github.com/Intelligent-Systems-Phystech/m1p_2022 GitHub]
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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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===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]
 
* [http://bit.ly/M1_2019_674 Group 674, spring 2019]
 
* [http://bit.ly/M1_2019_674 Group 674, spring 2019]
 
* [http://bit.ly/M1_2019_694 Group 694, spring 2019]
 
* [http://bit.ly/M1_2019_694 Group 694, spring 2019]
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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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<b>CONTENTS</b>
 
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[[Fundamental theorem]]|[[Todo list]]|[[Books]]|[[Reviews]]|[[Tools]]|[[Projects]]|[[Proposals]]|[[Templates]]|[[Career]]|[[Notation]]|[[Publication]]
 
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== The Art of Scientific Research ==  
 
== 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]'''-->
 
<!--'''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 dreams. We must be sure that the problem statement and project planning lead you to successful delivery according [[The Art of Scientific Research|to the syllabus]]. The repository template [https://github.com/vadim-vic/the-Art-homework helps].
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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]
 
 
* [[Step 0|Step 0: We start]]
 
* [[Step 0|Step 0: We start]]
 
* [[Step 1|Step 1: Highlight your work]]
 
* [[Step 1|Step 1: Highlight your work]]
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* [[Step 11|Step 11: The final talk]]
 
* [[Step 11|Step 11: The final talk]]
  
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==Mathematical forecasting, 2024==  
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==See also==
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* [[Fundamental theorems]] of ML
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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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* [https://m1p.org/jmlda JMLDA archive]<!--|[[Todo list]]|[[Books]]|[[Reviews]]|[[Tools]]|[[Projects]]|[[Proposals]]|[[Templates]]|[[Career]]|[[Notation]]|[[Publication]]-->
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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>
 
 
<strong>MediaWiki has been installed.</strong>
 
<strong>MediaWiki has been installed.</strong>
 
Consult the [https://www.mediawiki.org/wiki/Special:MyLanguage/Help:Contents User's Guide] for information on using the wiki software.
 
Consult the [https://www.mediawiki.org/wiki/Special:MyLanguage/Help:Contents User's Guide] for information on using the wiki software.

Latest revision as of 17:02, 2 July 2025

My first scientific paper

 

News

Fall 2025 — Functional Data Analysis starts soon

Before 13 February 2026 — My first scientific paper: Suggest your project here

On Thursdays at 17:50 —  Class m1p.org/go_zoom and discussion channel t.me

See results of 2024 —  on GitHub

Foundation AI Models for Science, 2025

Foundation AI models are universal models to solve a wide set of problems. This project proposes to investigate the theoretical properties of foundation models. The domain to model is a spatial-time series. These data are used in various scientific disciplines and serve to generalise scientific knowledge and make forecasts. The essential problems, formulated as user requests that solve a foundation model, are forecasting and generation of time series; analysis and classification of time series; detection of change point, and causal inference. To solve these problems, the foundation AI models are trained on massive datasets. The main goal of this project is to compare various architectures of foundation models to find an optimal architecture that solves the listed problems for a wide range of spatial time series. See the FDA page.

Mathematical forecasting, 2025

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. See the page

My first scientific paper

This course produces student research papers. It gathers research teams. Each team consists of 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 way to the goal. The projects start in February and end in May, according to the schedule.

Links

The Art of Scientific Research

The goal is to select and prepare the research topic of your dreams. We must be sure that the problem statement and project planning lead you to successful delivery according to the syllabus. The repository template helps.

See also


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