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

Материал из MachineLearning.

(Различия между версиями)
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(Examples and references)
(Examples and references)
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* [https://towardsdatascience.com/questions-96667b06af5#dee8 TDS guidelines]
* [https://towardsdatascience.com/questions-96667b06af5#dee8 TDS guidelines]
* [https://nplus1.dev/blog/2022/04/01/samotek N+1 samotek]
* [https://nplus1.dev/blog/2022/04/01/samotek N+1 samotek]
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Версия 09:21, 8 сентября 2022

Each Saturday 13:10 at the channel m1p.org/go_zoom


Intelligent data analysis

This course develops skills of communication. The goal is to deliver your message to wide auditory of professionals. The form of delivery is a short paper. It results several discussions in our team according to the plan below.

Schedule and grading

Workflow

  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)

Calendar

  • Sep: 16, 23, 30 select
  • Oct: 7, 14, 21, 28 talk
  • Nov: 4, 11 talk, 18, 25 text
  • Dec: 2 link, 9 fin

Insert your name and direct link to materials. Each column must carry your name.

Date Select Talk Text
16nxt Islamov, Strijov
23sep ...
30 ...
7oct x ...
14 ...
21 ...
28 ...
4nov ...
11 ...
18 x ...
25 x ...

Course page, and projects


The result links before 2nd of december

Topics to discuss

  • Differential alignment of continuous-time (series) videos [2104.13478]
  • Taken's theorem and convergent cross-mapping (signals) [or 2208.10981]
  • Graph diffusion models with PDE examples (flows, signals,videos) [2106.10934]
  • or probabilistic diffusion models [2208.11970]
  • Dimensionality reduction on Riemannian manifolds (for videos) [1605.06182]
  • Applications of Lagrangian, Hamiltonian and Noetherian neural PDEs [colab Severilov] [or 2208.06120]

Examples and references

Личные инструменты