esokolov/ml-course-hse

ml-course-hse is a repository of course materials for a machine learning class at the Faculty of Computer Science at the Higher School of Economics in Moscow, Russia. The course runs across two sequential semesters and is part of the undergraduate program in Applied Mathematics and Computer Science. The materials include lecture notes, seminar session handouts, theoretical homework problems, practical coding assignments, and links to Kaggle-style competitions associated with the course. The repository is primarily in Russian. Video recordings from the 2020-2021 academic year are linked in the README, covering both the fall and spring semesters. The fall semester recordings address topics such as linear regression, gradient descent methods, logistic regression, support vector machines, decision trees, random forests, gradient boosting, clustering, dimensionality reduction, and recommendation systems. Each major topic has its own YouTube video, and a full playlist groups them together. Recordings from the 2018-2019 academic year cover several of the same topics and are linked separately for those who prefer an earlier presentation style. Course administrative information, including grading details and assignment schedules, lives on the university wiki at cs.hse.ru. The GitHub repository holds the actual content files: Jupyter Notebooks, PDF documents, and other materials that students download and work through on their own machines. This repository is useful primarily for Russian-speaking students studying machine learning who want a structured curriculum covering both foundational theory and hands-on implementation. It is a collection of educational documents and notebooks rather than a software library or installable tool, and there is no setup required beyond downloading the files.