Instructor: Rafael Ramirez
Credits: 5 ECTS

Machine learning has proved to provide efficient solutions to many music-related problems both of academic and commercial interest. This course of the Master in Sound and Music Computing focuses on machine learning and deep learning techniques and their application to audio and music technology. The expected outcome of the course is a deep understanding of the main machine learning techniques and their relevance in audio and music applications. The course promotes and provides opportunities to discuss ongoing and state-of-the-art research in the area.

The course is offered in 20 weeks, with 30 hours of lectures and seminars. The evaluation of the students is based on assignments, oral presentations and student participation.

All the materials prepared for the class are available in:

Topics covered

  • Introduction to Machine Learning
  • Linear Regression
  • Logistic Regression
  • Artificial Neural Networks
  • Deep Neural Networks (CNNs, RNNs)
  • Support Vector Machines
  • Decision/Regression Trees
  • Instance-based Learning
  • Genetic Algorithms/Programming
  • Inductive Logic Programming
  • Clustering
  • Ensemble Methods
  • Feature Selection

      and their applications to Music Technology