Machine learning course at MIPT

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

• basic track (Spring 2019): youtube playlist
• advanced track (Fall 2019): youtube playlist

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.

Extra theoretical materials

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 topicks

Docker image

Using docker for tasks evaluation is a good idea, prebuilt image is under cunstruction