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Guide · Open source

How to use PRML: run Bishop's pattern recognition code in Python

Updated · written with AI from the project's own documentation and checked against our catalogue data

PRML is an open-source repository of Python code that implements the algorithms described in Christopher Bishop's book Pattern Recognition and Machine Learning. It is organized as Jupyter notebooks, one per chapter. This guide shows you how to get the code, open the notebooks, and read the methods alongside working code.

PRMLctgk/PRML · ★ 11.7k · MIT

Quick start

  1. 1Clone the repository. Run git clone https://github.com/ctgk/PRML to download the code.
  2. 2Create the environment. Run conda env create -f environment.yaml. The README notes this step might be optional.
  3. 3Activate the environment. Run conda activate prml.
  4. 4Install the package. Run python setup.py install from the repository folder.
  5. 5Open a chapter notebook. Pick the chapter you are reading from the README table and open it in nbviewer or SageMaker Studio Lab.

What PRML is and who it is for

PRML is a set of Jupyter notebooks that implement the algorithms in Bishop's book. Each notebook follows one chapter, from introduction and probability distributions through neural networks, graphical models, and sequential data.

It suits students working through the book, readers who learn by stepping through implementations, developers who want to see classic pattern recognition methods in plain Python, and instructors who want chapter-by-chapter examples. It is not a production machine learning library and the README does not describe it as an installable package with an API.

Install PRML from the clone-and-build steps

The repository is installed by cloning it and then building it with the commands in the README. You need Python 3, numpy and scipy. Jupyter is optional for running notebooks, matplotlib is optional for plotting results, and sklearn is optional for fetching data.

The README lists these commands. The environment file is marked as possibly optional.

  • Clone the repository: git clone https://github.com/ctgk/PRML
  • Create the conda environment (the 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
git clone https://github.com/ctgk/PRML conda env create -f environment.yaml # might be optional conda activate prml python setup.py install

Open the notebooks for each chapter

The notebooks can be viewed with nbviewer, or opened in Amazon SageMaker Studio Lab, which the README describes as a free computing environment on AWS. Studio Lab requires prior registration with an email address.

The README table links each chapter, from ch1 Introduction through ch13 Sequential Data, to both viewing options. Pick the chapter that matches the part of the book you are reading.

  • ch1 Introduction
  • ch2 Probability Distributions
  • ch3 Linear Models for Regression
  • ch4 Linear Models for Classification
  • ch5 Neural Networks
  • ch6 Kernel Methods
  • ch7 Sparse Kernel Machines
  • ch8 Graphical Models
  • ch9 Mixture Models and EM
  • ch10 Approximate Inference
  • ch11 Sampling Methods
  • ch12 Continuous Latent Variables
  • ch13 Sequential Data

Run PRML on SageMaker Studio Lab

If you use Studio Lab, the README says to open a terminal and run the install commands there to install the required libraries. This avoids setting up Python on your own machine.

The README links an AWS document for Studio Lab usage. Refer to that document for account and onboarding details.

conda env create -f environment.yaml # might be optional conda activate prml python setup.py install

Read the book and code together

The most practical way to use PRML is to open the notebook for a chapter while reading the same chapter in the book. Step through the code cells, change inputs, and compare the output with the method as described.

Because the notebooks are organized around the book's chapters, you will need the book for context. The repository is not a standalone tutorial.

What people use it for

  • Working through a chapter of Bishop's book with runnable code beside the text
  • Stepping through how a classic pattern recognition method is written in plain Python
  • Preparing chapter-by-chapter examples to accompany the textbook in a course
  • Comparing the notebook code with the equations in the book

Frequently asked questions

Is PRML free to use?

PRML is released under the MIT licence, as listed on the repository. The README also mentions Amazon SageMaker Studio Lab as a free computing environment, which requires registration.

What do I need to run PRML?

The README lists Python 3, numpy and scipy as required. Jupyter is optional for running notebooks, matplotlib is optional for plotting, and sklearn is optional for fetching data.

Do I need the Bishop book to use PRML?

The notebooks are organized around the book's chapters, so they are designed to accompany it. Without the book, the notebooks are harder to follow as a standalone tutorial.

Can I install PRML with pip?

The README gives a clone-and-build route: clone the repository, create the conda environment, activate it, and run python setup.py install. It does not describe a pip package.

Is PRML suitable for production machine learning?

No. PRML is a companion to a textbook, and the README does not describe it as a maintained package. For production work, a dedicated library such as scikit-learn is a better fit.

How does PRML compare with scikit-learn or PyTorch tutorials?

PRML focuses on implementing the algorithms in one textbook, step by step in notebooks. scikit-learn is a library with an API for applied use, and PyTorch tutorials focus on a deep learning framework. Choose PRML to learn the underlying methods from Bishop.

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Our verdict

Use PRML if you are studying Bishop's book and want readable Python code for each chapter, or if you teach from it. Pick a maintained library such as scikit-learn if you need a tool for real projects, or a standalone course if you do not have the book.

Install options & downloads