Omar Costa Hamido
has successfully completed an online offering of
Machine Learning for Musicians and Artists
Taught by
Rebecca Fiebrink
Course Run Dates: February 3, 2016 — May 25, 2016
Issued: May 23, 2016

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