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Machine Learning for Musicians and Artists

Goldsmiths University of London via Kadenze


Have you ever wanted to build a new musical instrument that responded to your gestures by making sound? Or create live visuals to accompany a dancer? Or create an interactive art installation that reacts to the movements or actions of an audience? If so, take this course!

In this course, students will learn fundamental machine learning techniques that can be used to make sense of human gesture, musical audio, and other real-time data. The focus will be on learning about algorithms, software tools, and best practices that can be immediately employed in creating new real-time systems in the arts.

Specific topics of discussion include:

• What is machine learning?

• Common types of machine learning for making sense of human actions and sensor data, with a focus on classification, regression, and segmentation

• The “machine learning pipeline”: understanding how signals, features, algorithms, and models fit together, and how to select and configure each part of this pipeline to get good analysis results

• Off-the-shelf tools for machine learning (e.g., Wekinator, Weka, GestureFollower)

• Feature extraction and analysis techniques that are well-suited for music, dance, gaming, and visual art, especially for human motion analysis and audio analysis

• How to connect your machine learning tools to common digital arts tools such as Max/MSP, PD, ChucK, Processing, Unity 3D, SuperCollider, OpenFrameworks

• Introduction to cheap & easy sensing technologies that can be used as inputs to machine learning systems (e.g., Kinect, computer vision, hardware sensors, gaming controllers)


  • Introduction
    • What is machine learning? And what is it good for? We’ll introduce a variety of artistic, musical, and interactive applications in which machine learning can help you create new things.
  • Classification, Part II; Design Considerations
    • In this session, we’ll take a deeper look at what it means to build a good classifier, and we’ll explore some common and powerful classification algorithms, including decision trees, Naive Bayes, AdaBoost, and support vector machines. We’ll also dig deeper into an exploration of how learning algorithms can be integrated into your own work most easily to achieve your desired outcomes. You’ll get a chance to explore these new algorithms and continue to work them into your own projects.
  • Working with Time
    • In this session, we’ll talk about algorithms that have been specifically designed to help you make sense of changes in data over time. Rebecca will dive into dynamic time warping, and guest lecturer Baptiste Caramiaux will discuss Gesture Variation Follower, an algorithm designed with the arts in mind. You’ll continue to get plenty of opportunities to apply temporal modeling algorithms to real-time data analysis.
  • Classification, Part I
    • In this session, we’ll cover the basics of classification, which can be used to make sense of complex data in a meaningful way. We’ll look at two classification algorithms: nearest-neighbor and decision stumps. You’ll be introduced to the Wekinator, a free software tool for using machine learning in real-time applications.
  • Sensors and Features: Generating Useful Inputs for Machine Learning
    • Machine learning makes it easier and more fun to work with all sorts of real-time sources of data, including real-time audio, video, game controllers, sensors, and more! We’ll talk about good strategies for making sense of the data you’ll get from different inputs, and for designing feature extractors that make machine learning easier. We’ll be encouraging students to develop their own feature extractors and share them with each other!
  • Developing a Machine Learning Practice; Wrap-up
    • Guest lecturer Laetitia Sonami will give a masterclass in which she discusses the way machine learning fits into her own work building new musical instruments, and Rebecca will discuss practical tools, boos, and resources you can access for furthering your work in this field.
  • Regression
    • We will discuss the fundamentals of regression, which can be used for creating continuous mapping and controls. We’ll explore the use of linear regression, polynomial regression, and neural networks to create new types of interactions. You’ll gain hands-on practice exploring regression algorithms and starting to apply them to build your own systems.

Taught by

Rebecca Fiebrink


4.8 rating, based on 93 Class Central reviews

Start your review of Machine Learning for Musicians and Artists

  • In the first place, it seemed to me to be a fundamental course because it is a subject that is not very widespread and at the same time very avant-garde. Secondly, despite the complexity of the topics covered, the approach is very simple and makes it possible to approach the topics with very little prior knowledge. Finally, it seems fundamental to me that this theoretical knowledge is supported by free and open access tools. I would also like to make a special mention to Rebecca Fiebrik for her great contribution.
  • Terrific class for a person looking to bring interactivity to music or visual art. It's also a great introduction to machine learning that goes deep enough to give you an understanding of the tools without taking you ALL the way into a very deep su…
  • Anonymous
    This course was probably good when it first went up, but at this point many of the software tools no longer work on more recent operating systems. I spent more time modifying software to try and get it to run (with mixed results) than I did working with the AI algorithms. I've also had trouble uploading some assignments.

    There is ABSOLUTELY NO SUPPORT. I've filed multiple support tickets with Kadenze and asked for assistance on the forums and have never gotten a reply.

    The lectures are very good, but that's it.
  • Anonymous
    Really appreciate the material, good insights about how to use this concepts in the creative industry. Recommended.
  • Anonymous
    The course "Machine Learning for musicians and artists" is focused on music production but computational science: machine learning. I've never been good at math, but knowing computational methods from the idea of ​​music production is a different w…
  • Gus
    I am doing this course and overall, I've found it to be quite a positive experience. It seems well-structured and provides clear objectives for each module. The content is relevant and up-to-date, except for some of the code, which is quite understa…
  • Anonymous
    so coool!! very informative, skips right to all the fun part, uses interesting case studies and examples. good topic coverage. instructor is engaging. thumbs up!
  • Anonymous
    This course on Machine Learning for Musicians and Artists was a splendid introduction to the basics and workings of most of the basic machine learning (ML) algorithms. Rebecca Fiebrink is a genious lecturer that not only built the tool (Wekinator) t…
  • Anonymous
    For me, a dream course which puts together some long standing areas of interest. Pragmatically, this course gives you the tools to introduce meaningful gestural control or input to digital music (my interest) as well as a range of other applications…
  • Profile image for Alexander Solovets
    Alexander Solovets
    The class is very lightweight, yet gives a solid understanding of how one can apply physic-based models to generate natural looking sound effects. I appreciate that choice of programming language, because the class listeners don't have to waste their time developing building blocks from scratch. I also liked authentic environment used by the lecturer as well as clear and noiseless picture and audio of the lectures. I recommend this class to anyone interested in game development or procedural content generation.

    UPD: sorry, this review is for another course from Kadenze.
  • Anonymous
    I am a computational artist and PhD student in computer science and this course is the best I have ever seen to teach machine learning. The instructor, Rebecca Fiebrink, is engaging and makes learning the logic models MUCH easier by teaching them in the context of making interactive art. I would recommend this course to anyone with at least a beginner's level knowledge of a creative coding language like openFrameworks or Processing. Fiebrink provides lots of example code for you to use so you don't need to write any code of your own from scratch to get started.
    Es la primera vez que encuentro un curso donde puedes usar AI para desarrollo de proyectos interactivos, al igual que muchos más de interés para artistas, como max, computacional creativity entre otros, que buenos cursos y fáciles de entender. Los maestros son personajes que estuvieron dentro del equipo creativo de desarrollo de los proyectos desde el inicio o que se dedican de lleno al tema. Vale lo que se paga por la membresía premium.
  • Anonymous
    Simply the best and most inspiring introduction to ML that exists out there. Rebecca manages to take creative students all through the landscape, starting from scratch and giving a hands-on experience that enables newbies to experiment creatively from the outset.
    I've given the link to several of my students, and I'm happy to say that the course has been a seminal turnaround point for several of them and their later studio practice as graduates.
  • Anonymous
    This is not a good class to take. The skills they teach can work in special scenarios, that aren't really used much in the real world. This is not worth hours of your time. Plus, the person who made the course forces you to use their program, so it is basically a 56 hour advertisement.
  • Anonymous
    I had alot of the suggested equipment so working on this class was straightforward. I appreciate that we focused more on training and use vs writing direct code, while still providing access to the code. It's somewhat of a challenge at first but once you get there it gets fun.
  • Anonymous
    Excellent course. Highly recommend. It was very helpful for anyone wanting to get into machine learning. Her explanations are clear and easy to understand without any mathematical knowledge.
  • Ron Kay
    Brilliant. I learned a lot and after that course I started to dig a lot deeper into Machine Learning.

    For me personally with a background in informatics the first two sessions started a bit slow but at session three it finally got the pace I enjoyed. But given that this course should reach a broad audience this isn't really a negative point.
  • Anonymous
    This course was super inspiring and open minding , as a musician I had so many great things to take from this course, and ever since I took it I try and incorporate Machine learning in my practices. great quality and great lecturer. Highly recommended
  • Anonymous
    This course gives an excellent introduction to machine learning, from an arts perspective. It gives you the ability to explore tools and concepts, hands on, learning by doing. It makes Machine Learning accessible and points the way to possibilities.
  • Anonymous
    Great course, very helpful and inspirational. I can recommend this course for anyone wanting to get into machine learning, particularly if you're interested in performance / realtime aspects of the field.

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