Deep Learning course, part  1 advanced track 

The advanced stream is aimed primarily at students who have a background in math and programming and want to dive deep into deep learning. We will start with the basics of machine learning, and then we will talk in detail about deep learning and its most interesting and latest applications. There will be a lot of fascinating theory and even more practice. To receive benefits for admission to the MIPT master's program, you must successfully complete the advanced stream.

Our course is divided into two semesters. Here you can see the program of the first semester.

At the end of each semester, students take an individual project

(project topics will be announced towards the end of the semester).

The course is hosted on the Stepik platform. The registration is open until February 13th.  

The first lesson will be avaliable on February 12th.

Course program

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  1. Fundamentals of machine learning, sklearn library

  2. Linear Models, OOP in Machine Learning

  3. Algorithm Compositions and Model Selection Methods

  4. Introduction to Neural Networks and PyTorch library

  5. Fundamentals of Convolutional Neural Networks

  6. Methods for training neural networks

  7. Convolutional Neural Network Architectures and Image Classification

  8. Image segmentation

  9. Image detection

  10. Practical application and implementation of computer vision models

  11. Generative Models and Autoencoders

  12. Generative adversarial models

  13. Competitions on Kaggle

  14. Additional lectures from our partners

  15. Final project

Unsure which track to choose?

Visit our FAQ page