An Introduction to Markov Chains: Machine Learning in Max/MSP

Difficulty level: Beginner


Markov chains are mathematical models that have existed in various forms since the 19th century, which have been used to aid statistical modelling in many real-world contexts, from economics to cruise control in cars. Composers have also found musical uses for Markov Chains, although the implied mathematical knowledge needed to implement them often appears daunting.

In this workshop we will demystify the Markov Chain and make use of the popular library in Max/MSP to implement Markov Chains for musical composition. This will involve preparing and playing MIDI files into the system (as a form of Machine Learning) and capturing the subsequent output as new MIDI files. By the end of the session you will have the knowledge of how to incorporate Markov Chains into your future compositions at various levels.


  • Max
  • Markov Chains
  • Machine Learning
  • Algorithmic Composition


  • You should have a basic understanding of the Max workflow and different data types.
  • Knowledge of MIDI format and routing to DAWs (Ableton, Logic etc) would be a plus, although Max instruments will be provided.
  • No prior knowledge of advanced mathematical or machine learning concepts are necessary, the focus will be on musical application.

About the workshop leader

Samuel Pearce-Davies is a composer, performer, music programmer and Max hacker living in Cornwall, UK.

With a classical music background, it was his introduction to Max/MSP during undergraduate studies at Falmouth University that sparked Sam’s passion for music programming and algorithmic composition.

Going on to complete a Research Masters in computer music, Sam is now studying a PhD at Plymouth University in music-focused AI.