# probml/pml2-book

Probabilistic Machine Learning: Advanced Topics

Repository: https://github.com/probml/pml2-book
Canonical: https://ross.abutalabs.com/products/pml2-book
License: MIT
License Family: permissive
Last push: 2026-05-26T09:15:27+00:00

## Health v2 (maintenance only)
Score: 70/100 (v2, computed 2026-09-03T02:20:16.233290+00:00)
- activity 84, release rhythm 36, longevity 100
- inputs: {"age_days": 1648, "days_push": 99, "days_rel": 266, "gap_med": 143, "n_releases_24m": 4}
- flags: none
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 1541, forks 139 (observed 2026-08-28T04:05:00.473179+00:00)

## What it is
The official repository hosting the PDF of 'Probabilistic Machine Learning: Advanced Topics' by Kevin Murphy, released via GitHub releases. It serves as a distribution and issue-tracking point for the book, complementing its companion volume on probabilistic machine learning.

## Use cases
- download a free advanced machine learning textbook pdf
- learn probabilistic machine learning topics
- report errors or issues with the pml2 book
- study bayesian and generative modeling theory
- find a reference for graduate-level ml coursework

## When to choose
- you want a comprehensive, freely available textbook on advanced probabilistic ML
- you need a rigorous theoretical reference for Bayesian or generative methods
- you want to track updates and download the latest book edition

## When to avoid
- you need runnable code or software rather than a book
- you want introductory machine learning material rather than advanced topics
- you need an interactive course with exercises and grading

## Facets
- artifact type: learning-resource
- maturity: active
- function: machine-learning, documentation
- domain: machine-learning, artificial-intelligence, tutorials
- platform: cross-platform
- tags: textbook, probabilistic-machine-learning, pdf-book, kevin-murphy, statistics

## Member repositories
- probml/pml2-book (main) score 70

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:05:00.473179+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 2026-08-30T04:30:48.683029+00:00, confidence not recorded.
  - readme: https://github.com/probml/pml2-book (fetched 2026-08-28T04:05:00.473179+00:00, sha 5029ff18d8f7)
- Data as of 2026-08-30T08:39:29.467469+00:00.