This course aims to introduce students to modern state of Machine Learning and Artificial Intelligence. It is designed to take one year (two terms at MIPT) - 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
Organizational information about current launches available at ml-mipt.github.io
- on
masterbranch previous term materials are stored to give a quick and comprehensive overview - on
basicandadvancedbranches materials for current launches are being published - tags (e.g.
spring_2019) contain previous launches materials for convenience
All lecture materials are currently in Russian language
- [ru] advanced track (updated) (Fall 2020):
Lectures youtube playlist,Practice sessions youtube playlist - [ru] basic track (Spring 2020, updated):
lectures youtube playlist (ru),practice sessions youtube playlist (ru) - [ru] advanced track (Fall 2019):
lectures youtube playlist (ru),practice sessions youtube playlist (ru) - [ru] basic track (Spring 2019):
lectures youtube playlist (ru)
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.
A lot of great materials are available online. See extra_materials.md file for the whole list.
Informal "aggregation" of all topics by previous years students: file (in Russian) - useful for fast and furious exam passing.
Also lectures and seminars contains references to more detailed materials on topics.
Using docker for tasks evaluation is a good idea, prebuilt image is under cunstruction.