Awesome Open Source
Awesome Open Source

Machine Learning at MIPT

Basic (first semester) and advanced (second semester) tracks, Spring 2021

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

Repository structure

  • on master branch previous term materials are stored to give a quick and comprehensive overview
  • on *_basic and *_advanced branches 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


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.

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