Difference between revisions of "My first scientific paper"

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=== m1p Week 8 ===
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==Functional Data Analysis, 2025==  
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* [[Week 10|Homework]]
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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 a latent state space. This space aligns the source and target spaces and generates data in the source and target manifolds. [[Functional Data Analysis|See the FDA page]].
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* [https://forms.gle/YnEnGhuPbS4aGhSw7 Questionnaire]
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==Mathematical forecasting, 2025==
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<!--[https://forms.gle/SLAs8nmZZaseQEw17 Questionnaire]-->
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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 MF page]]
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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-->
 
  
 
== m1p Course progress==
 
== m1p Course progress==
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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 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.  
  
 
* [[The Art of Scientific Research|The course syllabus]]
 
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* [[Step 11|Step 11: The final talk]]
 
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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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*All questions to <strong>mlalgorithms [at] gmail [dot] com,</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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Revision as of 11:50, 2 May 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

Functional Data Analysis, 2025

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 \(\frac{d\mathbf{x}}{dt}\), 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, \(\frac{d^2x}{dt^2}=-c\sin{x}\). We use physics-informed neural networks to approximate data. Fifth, the practical experiments involve multiple data sources. We use canonical correlation analysis with a latent state space. This space aligns the source and target spaces and generates data in the source and target manifolds. 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 MF page

m1p 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


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.

MediaWiki has been installed. Consult the User's Guide for information on using the wiki software.

Getting started

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