Creative Coding
An Introduction to Markov Chains: Machine Learning in Max
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
Course overview
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.
Learning outcomes
Who is this course for?
- • This course is for musicians interested in getting creative with their compositions by using Markov Chains.
Requirements
- • 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.
Course content
Course Overview
1 resource, 2 lessons
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Course Overview
1 resource, 2 lessons
What you will learn in this course
Markov chains are mathematical models that have existed in various forms since the 19 th 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 ml.star 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.
Topics
- Max
- Markov Chains
- Machine Learning
- Algorithmic Composition
Requirements
Difficulty level: Beginner
Requirements
- 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.
- Session Materials
An Introduction to Markov Chains: Machine Learning in Max/MSP - LIVE session
9 videos
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An Introduction to Markov Chains: Machine Learning in Max/MSP - LIVE session
9 videos
Part 1 - A Non-Musical Probability Example
Checking access...Part 2 - Building a Basic Markov Chain from Scratch
Checking access...Part 3 - Implementing ml.markov and Using a Longer Melody to Explore Markov Chain Order
Checking access...Part 4 - Training on MIDI Data
Checking access...Part 5 - Increasing the Complexity of the Markov Chain Setup: Velocity
Checking access...Part 6 - Increasing the Complexity of the Markov Chain Setup: Chords
Checking access...Part 7 - Introducing the Finished Markov Chain with User Interface
Checking access...Part 8 - Blending Musical Data from Different MIDI Files
Checking access...Part 9 - Additional Examples and Blending
Checking access...
Instructors
