Vincentius Madya
has successfully completed an online offering of
Machine Learning for Musicians and Artists
Taught by
Rebecca Fiebrink
Estimated Effort: 50 hours
Issued: December 21, 2020

Learning Outcomes

​Hands-on proficiency applying machine learning for creating real-time interactions

  • Ability to use machine learning for real-time analysis of audio, video, gesture, and sensors
  • Ability to use machine learning to build real-time controllers for music, games, and interactive art

Computational processes in machine learning

  • Understanding of different algorithmic strategies for creating models from data
  • Familiarity with widely useful machine learning algorithms for classification, regression, and temporal modeling
  • Ability to match machine learning algorithms to real-world problems, to reason about tradeoffs between different algorithms, and to evaluate, debug, and improve machine-learned systems

Practical and aesthetic considerations in applying machine learning to artistic problems

  • Understanding of how machine learning can be used in the arts and music, and exposure to different artistic practices using machine learning
  • Understanding of how machine learning for creative and real-time applications is different from (and similar to) machine learning in more conventional applications, and translating that understanding into effective approaches to machine learning practice

Authenticity of this Certificate of Accomplishment can be verified at: https://www.kadenze.com/certificates/verified/MLFFCG0G
To receive a Certificate of Accomplishment, participants must meet all course requirements and be able to demonstrate required knowledge and skill through online assessments. Verified: To ensure academic integrity, the identity of all registered members is verified by Kadenze on an ongoing basis. Please note: Some online courses may draw on material from courses taught on-campus but they are not equivalent to on-campus courses. Participants who are currently enrolled or have completed courses offered by Kadenze or any Kadenze institutional partner are not to be considered registered students of the issuing universities. This statement does not affirm that this participant was enrolled as a student at Goldsmiths University of London in any way. It does not confer a Goldsmiths University of London grade, course credit, or degree, and Goldsmiths University of London has not verified the identity of the participant.
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