Machine Learning at MIPT
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
Repository structure
- on
master
branch previous term materials are stored to give a quick and comprehensive overview - on
basic
andadvanced
branches materials for current launches are being published - tags (e.g.
spring_2019
) contain previous launches materials for convenience
Video lectures
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)
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.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.
Docker image
Using docker for tasks evaluation is a good idea, prebuilt image is under cunstruction.