# gerdm/prml

Repository of notes, code and notebooks in Python for the book Pattern Recognition and Machine Learning by Christopher Bishop

Repository: https://github.com/gerdm/prml
Canonical: https://ross.abutalabs.com/products/gerdm-prml
Language: Jupyter Notebook
License: AGPL-3.0
License Family: copyleft
Topics: machine-learning, prml, bayesian-statistics, python, pattern-recognition
Last push: 2022-07-25T04:36:22+00:00

## Health v2 (maintenance only)
Score: 32/100 (v2, computed 2026-09-02T17:46:02.011165+00:00)
- activity 0, release rhythm 35, longevity 100
- inputs: {"age_days": 2840, "days_push": 1500, "days_rel": null, "gap_med": null, "n_releases_24m": 0}
- flags: no_releases
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 2626, forks 544 (observed 2026-08-28T04:07:05.354223+00:00)

## What it is
A collection of Jupyter notebooks implementing algorithms and reproducing figures from Christopher Bishop's book 'Pattern Recognition and Machine Learning'. It serves as a study companion covering topics from Bayesian inference to graphical models and sampling methods.

## Use cases
- study machine learning algorithms from the PRML book with runnable code
- learn Bayesian linear regression and Gaussian processes through notebooks
- understand variational inference and MCMC sampling with worked examples
- reproduce figures from Bishop's Pattern Recognition and Machine Learning book
- prepare for machine learning coursework or interviews on classical ML topics
- explore implementations of SVMs, GMMs, k-means, and neural networks

## When to choose
- you are reading the PRML book and want code alongside each chapter
- you prefer learning classical Bayesian machine learning through interactive notebooks
- you want reference implementations of algorithms like EM, Gibbs sampling, and Gaussian processes

## When to avoid
- you need a production-ready machine learning library
- you want modern deep learning frameworks or GPU-accelerated training
- you need maintained, actively developed software with support

## Facets
- artifact type: learning-resource
- maturity: maintenance
- function: machine-learning, data-science, math
- domain: machine-learning, tutorials, education
- platform: python
- tags: jupyter-notebooks, bayesian-statistics, pattern-recognition, bishop-prml, educational, algorithms

## Member repositories
- gerdm/prml (main) score 32

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:07:05.354223+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 2026-08-30T02:19:47.044540+00:00, confidence not recorded.
  - readme: https://github.com/gerdm/prml (fetched 2026-08-28T04:07:05.354223+00:00, sha bee26c463f1f)
- Data as of 2026-08-30T08:39:29.467469+00:00.