Warning, repository has been renamed to represent its current status.
Open Machine Learning course
Basic (first semester) and advanced (second semester) tracks.
This course aims to introduce students to modern state of Machine Learning and Artificial Intelligence. It is designed to take one full year - approximately 2 * 15 lectures and seminars.
All learning materials are available here, full list of topics considered in the
course are listed in program_*.pdf files.
Repository structure
- on
masterbranch previous term materials are stored to give a quick and comprehensive overview - on
*_basicand*_advancedbranches materials for current launches are being published - tags (e.g.
2021_spring) contain previous launches materials for convenience
Video lectures
All lecture materials are currently in Russian language
| Track | Launch | Lectures | Practice | Language |
|---|---|---|---|---|
| basic | Spring 2020 | youtube | youtube | ru |
| advanced | Fall 2020 | youtube | youtube | ru |
| basic | Spring 2019 | youtube | - | ru |
| advanced | Fall 2019 | youtube | youtube | ru |
Prerequisites
We are expecting our students to have a basic knowlege of:
- calculus, especially matrix calculus, differentiation
- linear algebra
- probability theory and statistics
- programming, especially on Python
Although if you don't have any of this, you could substitude it with your diligence because the course provides additional materials to study requirements yourself.
Materials for self-study
A lot of great materials are available online. See extra materials file for the whole list.Also lectures and seminars contains references to more detailed materials on topics.
Lectures conspects are available for both basic and advanced. Especially useful for fast and furious exam passing. (Previous version of informal basic conspect is available)
Docker image
Using docker for tasks evaluation is a good idea, prebuilt image is under cunstruction.