Machine Learning for Musicians
education program·university course·active
Hands-on Berklee course for building music-machine-learning applications, including generative audio systems and neural audio tools.
Verified facts
| Official site | https://college.berklee.edu/courses/mtec-345 ↗ |
|---|---|
| Geography | United States |
| price | Included in Berklee tuition; course-specific price not disclosed |
| title | Machine Learning for Musicians |
| format | in_person |
| topics | music machine learning; generative audio; neural audio plugins; mixing and mastering tools; Jupyter; GitHub |
| duration | semester; 3 credits |
| end date | unknown |
| language | English |
| location | Boston, Massachusetts |
| provider | Berklee College of Music |
| recurrence | Fall and Spring |
| start date | unknown |
| instructors | Akito van Troyer |
| program type | undergraduate course |
| prerequisites | LMSC-261 Introduction to Computer Programming |
| program state | recurring |
| target audience | Berklee B.M. and professional-diploma students |
| learning outcomes | Take a music-ML project from concept to launch; build technical and creative music-AI applications |
| application deadline | Institutional registration; exact deadline not public |
| credential or outcome | 3 academic credits |
| scholarship and funding | not publicly disclosed |
Current
| featured by | Berklee Emerging Artistic Technology Lab |
|---|
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