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==Mathematical methods of forecasting, Fall 2022== | ==Mathematical methods of forecasting, Fall 2022== | ||
| − | This course delivers methods of | + | 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. |
==My first scientific paper, Spring 2023== | ==My first scientific paper, Spring 2023== | ||
Revision as of 11:49, 29 August 2022
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
Contents
This week at home
- Wait for it!
Mathematical methods of forecasting, Fall 2022
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.
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