Machine Learning for Musicians

MTEC-345

Machine learning is one of the most influential computing innovations shaping many aspects of our day-to-day lives today. Practical machine learning systems can abstract an understanding of data and the world around us to create new predictions for the future of manufacturing, entertainment, and mobility and generate new forms of creativity. In this course, students will learn the basics of machine learning and explore applications specific to music, art, and creativity. Students will acquire hands-on knowledge about how artworks and experiences can be created, composed, produced, performed, personalized, and recommended today using machine learning tools. Students will examine current machine learning tools and applications, then work in teams to develop their own projects. The class will include interventions and lectures from leaders in the field. Students will apply for this technology seminar so there will be a balance of technology skills in the class. The course is open to all students at both the college and the Conservatory and will be a mix of students with coding experience as well as a strong foundation in a variety of disciplines. The ideal cohort will be evenly divided between students who code and content experts in composition/songwriting, production, sound design, music business, and performance.

Credits
2
Prerequisites
Written approval of course instructor
Required Of
None
Electable By
All B.M. and P.D. students
Major Elective for
Electronic Production and Design
Semesters Offered
Fall, Spring
Location
Boston
Department
ELPD
Course Chair
Michele Darling
Taught By
Courses may not be offered at the listed locations or taught by the listed faculty for every semester. Consult my.berklee.edu to find course information for a specific semester.