# esokolov/ml-course-hse

Машинное обучение на ФКН ВШЭ

Repository: https://github.com/esokolov/ml-course-hse
Canonical: https://ross.abutalabs.com/products/ml-course-hse
Language: Jupyter Notebook
License Family: other
Last push: 2026-06-13T19:12:26+00:00

## Health v2 (maintenance only)
Score: 71/100 (v2, computed 2026-09-03T02:20:16.233290+00:00)
- activity 87, release rhythm 35, longevity 100
- inputs: {"age_days": 3650, "days_push": 81, "days_rel": null, "gap_med": null, "n_releases_24m": 0}
- flags: no_releases, no_license
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 3864, forks 1251 (observed 2026-08-28T04:08:27.624750+00:00)

## What it is
Course materials for the Machine Learning course taught at the Faculty of Computer Science, Higher School of Economics (HSE). Includes lecture notes, seminar materials, homework assignments, and video recordings in Jupyter Notebook format.

## Use cases
- learn machine learning fundamentals
- find university-level ML course homework assignments
- study linear regression and classification with lecture notes
- watch recorded ML lectures on gradient boosting and decision trees
- practice ML with theory and coding assignments
- self-study a complete ML curriculum

## When to choose
- you want a structured, university-quality ML course with notes, videos, and assignments
- you prefer learning through homework and contests alongside theory
- you are comfortable with Russian-language materials

## When to avoid
- you need production ML software or libraries
- you require English-only materials
- you need a license permitting redistribution of the content

## Facets
- artifact type: learning-resource
- maturity: active
- function: machine-learning
- domain: machine-learning, education, tutorials
- platform: python
- tags: university-course, lecture-notes, homework, jupyter-notebooks, hse, russian-language, education

## Member repositories
- esokolov/ml-course-hse (main) score 71

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:08:27.624750+00:00.
- Health v2: computed from the inputs above; adoption is never an input.
- Inferred fields (summary, facets, guidance): AI-extracted, prompt v1, taxonomy v1, on 2026-08-29T18:25:52.487319+00:00, confidence not recorded.
  - readme: https://github.com/esokolov/ml-course-hse (fetched 2026-08-28T04:08:27.624750+00:00, sha 366dce663cb9)
- Data as of 2026-08-30T08:39:29.467469+00:00.