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Revision as of 15:02, 9 February 2023

News and announcements

Spring 2024 in January — My fist scientific paper: Suggest your project here!

Spring 2024 in February 8th, 16:10 — My fist scientific paper starts

Fall 2023 Wednesday — Mathematical metods of forecasting

Each Thursday at 17:40 — the class My fist scientific paper m1p.org/go_zoom

My first scientific paper, Spring 2023

Homework for week 1

At the bottom of the pages

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 2023, according to the schedule.

Links

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

History

Mathematical methods of forecasting, Fall 2022

This course delivers methods of model selection in machine learning and forecasting. The modeling 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 organized as paper-with-code reports. See course page

@@ Line 2: / Line 2: @@
 {{Box|Title=News and announcements|Content={{News}}<!--''[[News|more]]''-->}}
-==Mathematical methods of forecasting, Fall 2022==
+==My first scientific paper, Spring 2023==
-This course delivers methods of model selection in machine learning and forecasting. The modeling 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 organized as paper-with-code reports. [[Mathematical forecasting|See course page]]
+===Homework for week 1===
+At the bottom of the pages
+*[[Week 0|Week 0: Come in]]
+*[[Week 1|Week 1: Set the toolbox]]
-==My first scientific paper, Spring 2023==
 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 2023, according to the [[Course schedule|schedule]].
@@ Line 45: / Line 47: @@
 * [http://bit.ly/M1_2019_674 Group 674, spring 2019]
 * [http://bit.ly/M1_2019_694 Group 694, spring 2019]
+==Mathematical methods of forecasting, Fall 2022==
+This course delivers methods of model selection in machine learning and forecasting. The modeling 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 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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