Difference between revisions of "Main Page"
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{{Box|Title=News and announcements|Content={{News}}<!--''[[News|more]]''-->}} | {{Box|Title=News and announcements|Content={{News}}<!--''[[News|more]]''-->}} | ||
+ | ==This week at home== | ||
+ | * Wait for it! | ||
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+ | ==My first scientific paper, Fall 2022== | ||
+ | This course delivers methods of multi-model selection in Machine learning. The modelling data is heterogenous: the source and the target are different video, audio, encephalogram, fMRI and other measurements in natural science. The models forecast one tensor field with another one. The field is space-time. It carries the measurements in the form of tensors. The practical ''examples'' are Brain-computer interfaces, weather and various spatial-time series forecasting. The ''lab works'' are organised as paper-with-code reports. | ||
+ | |||
+ | ==My first scientific paper, Spring 2023== | ||
This course produces student research papers. It gathers research teams in a society. Each team combines a student, a consultant and an expert. The student is a project driver, who wants to plunge into scientific research activities. The consultant, a graduated student, 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 of 2022 according to the [[Course schedule|schedule]]. | This course produces student research papers. It gathers research teams in a society. Each team combines a student, a consultant and an expert. The student is a project driver, who wants to plunge into scientific research activities. The consultant, a graduated student, 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 of 2022 according to the [[Course schedule|schedule]]. | ||
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*[[Course schedule]] | *[[Course schedule]] | ||
− | + | *[[Week 0|Week 0: Come in]] | |
*[[Week 1|Week 1: Set the toolbox]] | *[[Week 1|Week 1: Set the toolbox]] | ||
*[[Week 2|Week 2: Select your project and tell about it]] | *[[Week 2|Week 2: Select your project and tell about it]] | ||
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*[[Week 11|Week 11: Prepare your presentation]] | *[[Week 11|Week 11: Prepare your presentation]] | ||
*[http://www.youtube.com/watch?v=xW_lXGn1WHs Week 12: Show your results (Youtube)] | *[http://www.youtube.com/watch?v=xW_lXGn1WHs Week 12: Show your results (Youtube)] | ||
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==Links== | ==Links== |
Revision as of 11:01, 29 August 2022
Fall 2024 on September 14 — The Art of Scientific Research
Fall 2024 on September 13 — Functional Data Analysis
Before January 2025 — My fist scientific paper: Suggest your project here
Spring 2025 on February 6th — My fist scientific paper starts
See results of 2024 — on GitHub
Contents
This week at home
- Wait for it!
My first scientific paper, Fall 2022
This course delivers methods of multi-model selection in Machine learning. The modelling data is heterogenous: the source and the target are different video, audio, encephalogram, fMRI and other measurements in natural science. The models forecast one tensor field with another one. The field is space-time. It carries the measurements in the form of tensors. The practical examples are Brain-computer interfaces, weather and various spatial-time series forecasting. The lab works are organised as paper-with-code reports.
My first scientific paper, Spring 2023
This course produces student research papers. It gathers research teams in a society. Each team combines a student, a consultant and an expert. The student is a project driver, who wants to plunge into scientific research activities. The consultant, a graduated student, 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 of 2022 according to the schedule.
- Course schedule
- Week 0: Come in
- Week 1: Set the toolbox
- Week 2: Select your project and tell about it
- Week 3: State your problem
- Week 4: Plan the experiment
- Week 5: Visualise the principle
- Week 6: Write the theory
- Week 7: Analyse the error
- Week 8: Construct your paper
- Week 9: Review a paper
- Week 10: Select a journal to submit
- Week 11: Prepare your presentation
- Week 12: Show your results (Youtube)
Links
- 2022 results GitHub
- Telegram: discussion Ask here!
- The meeting room: m1p.org/go_zoom
- More courses from the MIPT Intelligent Systems
History (Ru)
- Main page with old homework
- Group 674, 694, spring 2020
- Group 674, spring 2019
- Group 694, spring 2019