Difference between revisions of "Main Page"

Latest revision as of 03:37, 7 February 2024

News and announcements

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

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

2023 problems GitHub
2022 results GitHub
Telegram: discussion Ask here!
The meeting room: m1p.org/go_zoom
More courses from the Intelligent Systems

History

Mathematical methods of forecasting, 2024

This course delivers methods of model selection in machine learning and forecasting. The modeling data are videos, audios, 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 course page

@@ Line 1: / Line 1: @@
-[[File:miai_logo.jpeg|class=img-responsive|left|alt Maths&AI MIPT-UGA student workshop]] &nbsp;
+{{#seo:
+ |title=Research management course
+ |titlemode=append
+ |keywords=Research management course
+ |description=This research management course immerses students in research activities that produce scientific papers.
+ }}
+[[File:Miai_logo1.jpeg|class=img-responsive|left|alt Maths&AI MIPT-UGA student workshop]] &nbsp;
 {{Box|Title=News and announcements|Content={{News}}<!--''[[News|more]]''-->}}
-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]].
+===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 [[Course schedule|schedule]].
-==Daily==
-* The lecture streams on Thursdays at 10:30msk
-* The seminar goes on Saturdays at 20:00msk
 *[[Course schedule]]
 *[[Week 0|Week 0: Come in]]
+** [https://forms.gle/hFiu8j3fHF9hdZkN8 Questionnaire 0]
+** [https://docs.google.com/document/d/1cESrKzU_kTL1PeRNhkQ1glzjbGh3VLw7Cbt34O19NGQ/edit?usp=sharing Scenario]
 *[[Week 1|Week 1: Set the toolbox]]
 *[[Week 2|Week 2: Select your project and tell about it]]
@@ Line 22: / Line 27: @@
 *[[Week 11|Week 11: Prepare your presentation]]
 *[http://www.youtube.com/watch?v=xW_lXGn1WHs Week 12: Show your results (Youtube)]
-<!--*[[Week 11|Week 11: Prepare your presentation]]
-*[[Week 10|Week 10: Select a journal to submit]]
-*[[Week 9|Week 9: Review a paper]]
-*[[Week 8|Week 8: Construct your paper]]
-*[[Week 7|Week 7: Analyse the error]]
-*[[Week 6|Week 6: Write the theory]]
-*[[Week 5|Week 5: Visualise the principle]]
-*[[Week 4|Week 4: Plan the experiment]]
-*[[Week 3|Week 3: State your problem]]
-*[[Week 2|Week 2: Select your project and tell about it]]
-*[[Week 1|Week 1: Set the toolbox]]
-*[[Week 0|Week 0: Come in]]
-*[[Course schedule]]-->
-==Links==
+===Links===
+*2023 problems [https://github.com/intsystems/m1p/blob/main-2023/problem_list.md  GitHub]
+*2022 results [https://github.com/Intelligent-Systems-Phystech/m1p_2022 GitHub]
 *Telegram: [https://t.me/m1p_org discussion] <b> Ask here! </b>
 *The meeting room: [https://m1p.org/go_zoom m1p.org/go_zoom]
-*More courses from the [https://m1p.org/is MIPT Intelligent Systems]
+*More courses from the [http://m1p.org/is Intelligent Systems]
 <!--
 *Check-1 and check-2 sing-in: [https://docs.google.com/spreadsheets/d/1g9ud_qyHJIkzHYWFRac_WmeVZ7Qv11N_OIXAzK5iOs0/edit#gid=298225191 table]
@@ Line 52: / Line 46: @@
 -->
-== History (Ru)==
+===History===
 * [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/M1_2019_674 Group 674, spring 2019]
 * [http://bit.ly/M1_2019_694 Group 694, spring 2019]
+==Mathematical methods of forecasting, 2024==
+This course delivers methods of model selection in machine learning and forecasting. The modeling data are videos, audios, 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 course page]]
 <!--*[http://bit.ly/m1p_file2discuss Upload a file to discussion]

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Difference between revisions of "Main Page"

Latest revision as of 03:37, 7 February 2024

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Mathematical methods of forecasting, 2024