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PRML

Python notebooks implementing the algorithms from Bishop's machine learning textbook, PRML.

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Stars
11.7k
Forks
3.2k
Licence
MIT
Language
Jupyter Notebook
Last update
1.6 years ago
Type
Learning resource

No commits in over a year — check for forks or alternatives before adopting.

What it is

This repository is a set of Jupyter notebooks that implement the algorithms described in Christopher Bishop's book "Pattern Recognition and Machine Learning." Each notebook follows one chapter of the book, from introduction and probability distributions through neural networks, graphical models, and sequential data. People use it to read the book's methods alongside working Python code.

  • Open the chapter 3 notebook to see linear regression implemented step by step
  • Run the chapter 9 notebook to explore mixture models and the EM algorithm
  • View any chapter notebook in nbviewer without installing anything
  • Open a chapter notebook in Amazon SageMaker Studio Lab for a free hosted environment

Great for

  • Students studying the Bishop PRML book who want runnable code for each chapter
  • Readers who learn best by stepping through algorithm implementations in notebooks
  • Developers who want to see how classic pattern recognition methods are written in plain Python
  • Instructors looking for chapter-by-chapter examples to accompany the textbook

Not ideal for

  • People who need a production machine learning library, since this is a companion to a textbook and the README does not describe it as a maintained package
  • Readers who do not have the book and want a standalone tutorial, because the notebooks are organized around the book's chapters
  • Anyone looking for an installable Python package with an API, as the README only lists required packages and a setup script
Step-by-step guide: how to use PRMLInstall, first run, examples, tips and FAQs

How to get it

  1. 1

    Clone and build from source

    You'll need: Python 3 · numpy · scipy · jupyter (optional, to run notebooks) · matplotlib (optional, to plot results)

    Clone the repository

    git clone https://github.com/ctgk/PRML

    Create the conda environment (README says this might be optional)

    conda env create -f environment.yaml

    Activate the environment

    conda activate prml

    Install the package

    python setup.py install
  2. 2

    Run it

    • Clone the repository and change into its folder
    • Install the required packages, or view notebooks without installing via nbviewer
    • Start Jupyter and open a chapter notebook from the notebooks folder
    • Run the cells in order to follow the chapter's algorithms
  3. 3

    Get the most out of it

    • Use the nbviewer links in the README to read chapters without any local setup
    • Open chapters in SageMaker Studio Lab to run them in a free hosted environment
    • Read each notebook alongside the matching chapter of Bishop's book
    • Install the optional matplotlib package to see the plotted results

Downloads

Source code (.zip)main branch · straight from GitHub

Strengths

  • Covers the full chapter sequence of a well-known machine learning textbook
  • Notebooks can be viewed in a browser through nbviewer without any local setup
  • Released under the MIT license, which permits reuse

Trade-offs

  • The README is short and does not explain how each implementation maps to the book's equations
  • Setup instructions are sparse; the install step is marked as possibly optional and the README gives no version requirements beyond "python 3"
  • The last push date is April 2025, so maintenance status is unclear from the README alone

How it compares

ProjectStarsTypeLevelUpdatedBest for
PRML (this)11.7kLearning resourcebeginner1.6 years agoStudents studying the Bishop PRML book who want runnable code for each chapter
homemade-machine-learning24.8kLearning resourcebeginner12 months agoStudents learning the math behind machine learning algorithms
numpy-10014.7kLearning resourcebeginner14 days agoBeginners who want hands-on practice with NumPy
open-notebook41.3kAppintermediate4 days agoPeople who want NotebookLM-style research tools without sending their material only to Google
ds-cheatsheets16.4kCurated listbeginner2.3 years agoBeginners who want quick reference sheets for Python or R

Stats synced from GitHub. The assessment above was drafted with AI from the project's README on 2026-10-29 and may miss recent changes — check the README before relying on a command. MIT licence: free to use, modify and distribute, including commercially, as long as you keep the copyright notice.