Matlab code for machine learning algorithms in book PRML

License: MIT

Language: MATLAB

Keywords: machine-learning, matlab, prml


This package is a Matlab implementation of the algorithms described in the classical machine learning textbook: Pattern Recognition and Machine Learning by C. Bishop (PRML).

Note: this package requires Matlab R2016b or later, since it utilizes a new syntax of Matlab called Implicit expansion (a.k.a. broadcasting in Python).


The design goal of the code are as follows:

  1. Succinct: Code is extremely terse. Minimizing the number of line of code is one of the primal target. As a result, the core of the algorithms can be easily spot.
  2. Efficient: Many tricks for making Matlab scripts fast were applied (eg. vectorization and matrix factorization). Many functions are even comparable with C implementation. Usually, functions in this package are orders faster than Matlab builtin functions which provide the same functionality (eg. kmeans). If anyone found any Matlab implementation that is faster than mine, I am happy to further optimize.
  3. Robust: Many numerical stability techniques are applied, such as probability computation in log scale to avoid numerical underflow and overflow, square root form update of symmetric matrix, etc.
  4. Easy to learn: The code is heavily commented. Reference formulas in PRML book are indicated for corresponding code lines. Symbols are in sync with the book.
  5. Practical: The package is designed not only to be easily read, but also to be easily used to facilitate ML research. Many functions in this package are already widely used (see Matlab file exchange).


  1. Download the package by running: git clone

  2. Run Matlab and navigate to package location as working directory, then run the init.m script.

  3. Run some demos in the your_location/demo directory. Enjoy!


If you found any bug or have any suggestion, please do fire issues. I am graceful for any feedback and will do my best to improve this package.


Currently Released Under GPLv3


sth4nth at gmail dot com

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