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 and advanced 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.