Интеллектуальный анализ данных (О.Ю. Бахтеев, В.В. Стрижов)/Осень 2022

Материал из MachineLearning.

(Различия между версиями)
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This course delivers methods of model selection in machine learning and forecasting. The modelling data are videos, audios, encephalograms, fMRIs and another 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 organised as paper-with-code reports.
This course delivers methods of model selection in machine learning and forecasting. The modelling data are videos, audios, encephalograms, fMRIs and another 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 organised as paper-with-code reports.
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==Schedule==
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==Schedule and grading==
*[Sep] 16, 23, 30
*[Sep] 16, 23, 30
*[Oct] 7, 14, 21, 28
*[Oct] 7, 14, 21, 28
*[Nov] 4, 11, 18, 25
*[Nov] 4, 11, 18, 25
*[Dec] 2, 9
*[Dec] 2, 9
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# Select topic (report)
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# Prepare material (present 5-10 min and discuss)
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# Make presentation (20 min and questions)
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# Write your text (2 pages and discuss)
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# Publish your text (link)
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==Course page, and projects==
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* TODO Course page
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* TODO Projects
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The result links
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* Bronstein, M. [Temporal Graph Networks https://medium.com/towards-data-science/temporal-graph-networks-ab8f327f2efe], Medium TDS
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==Topics to discuss==
==Topics to discuss==

Версия 08:17, 8 сентября 2022

Each tuesday 16:10 at the channel m1p.org/go_zoom


Intelligent data analysis

This course delivers methods of model selection in machine learning and forecasting. The modelling data are videos, audios, encephalograms, fMRIs and another 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 organised as paper-with-code reports.

Schedule and grading

  • [Sep] 16, 23, 30
  • [Oct] 7, 14, 21, 28
  • [Nov] 4, 11, 18, 25
  • [Dec] 2, 9
  1. Select topic (report)
  2. Prepare material (present 5-10 min and discuss)
  3. Make presentation (20 min and questions)
  4. Write your text (2 pages and discuss)
  5. Publish your text (link)

Course page, and projects

  • TODO Course page
  • TODO Projects

The result links

Topics to discuss

Examples and references

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