# epfml/ML_course

EPFL Machine Learning Course, Fall 2025

Repository: https://github.com/epfml/ML_course
Canonical: https://ross.abutalabs.com/products/ml_course
Homepage: https://www.epfl.ch/labs/mlo/machine-learning-cs-433/
Language: Jupyter Notebook
License Family: other
Last push: 2025-12-15T14:50:49+00:00

## Health v2 (maintenance only)
Score: 58/100 (v2, computed 2026-09-02T17:46:02.011165+00:00)
- activity 57, release rhythm 35, longevity 100
- inputs: {"age_days": 3703, "days_push": 261, "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 2016, forks 1014 (observed 2026-08-28T04:06:05.585835+00:00)

## What it is
The official repository for EPFL's Machine Learning course (CS-433, Fall 2025), containing lecture notes, labs, project templates, and solutions. It accompanies the course website and recorded lecture videos.

## Use cases
- learn machine learning fundamentals from a university course
- find machine learning course exercises and project templates
- study lecture notes for an intro ML class
- self-study machine learning with labs and solutions
- access recorded machine learning lectures
- prepare for a machine learning course project

## When to choose
- you want a structured, university-level introduction to machine learning
- you prefer learning through hands-on Jupyter notebook labs and projects
- you are an EPFL student following CS-433 or want equivalent material

## When to avoid
- you need production-ready machine learning libraries or tools
- you want advanced, research-level ML material
- you need software with a license permitting redistribution

## Facets
- artifact type: learning-resource
- maturity: active
- function: machine-learning, developer-tools
- domain: machine-learning, education, tutorials
- platform: python, cross-platform
- tags: university-course, jupyter-notebooks, lecture-notes, epfl, cs433, course-projects

## Member repositories
- epfml/ML_course (main) score 58

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:06:05.585835+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 2026-08-30T03:00:42.300675+00:00, confidence not recorded.
  - readme: https://github.com/epfml/ML_course (fetched 2026-08-28T04:06:05.585835+00:00, sha 3e67f3385d02)
  - homepage: https://www.epfl.ch/labs/mlo/machine-learning-cs-433/ (fetched 2026-08-29T10:40:59.190141+00:00, sha 26f79d894cd3)
  - site_page: https://www.epfl.ch/about (fetched 2026-08-29T10:40:59.199114+00:00, sha c1936c078d62)
  - site_page: https://www.epfl.ch/about/overview (fetched 2026-08-29T10:40:59.201073+00:00, sha 1aed64b7f991)
  - site_page: https://www.epfl.ch/about/campus (fetched 2026-08-29T10:40:59.202914+00:00, sha 3fe75775be11)
  - site_page: https://www.epfl.ch/about/facts (fetched 2026-08-29T10:40:59.204576+00:00, sha d0c021fc1423)
  - site_page: https://www.epfl.ch/about/presidency (fetched 2026-08-29T10:40:59.206227+00:00, sha 8c0032d399e3)
  - site_page: https://www.epfl.ch/about/vice-presidencies (fetched 2026-08-29T10:40:59.207756+00:00, sha 1998a512703a)
  - site_page: https://www.epfl.ch/about/working (fetched 2026-08-29T10:40:59.209257+00:00, sha 8a64cf464d06)
  - site_page: https://www.epfl.ch/about/recruiting (fetched 2026-08-29T10:40:59.210731+00:00, sha 3833a02cf154)
- Data as of 2026-08-30T08:39:29.467469+00:00.