Artificial Models for Multimodal Creativity: Part 1
- Studienfach
- CoPeCo, E-1-Jko-MM, Tec-W1-MMK, Tec-W2-MMK, W-bv, W-frei, Wiss-1-MMK
- Lehrende
- Alessandro Anatrini
- Termin
- Part 1 Schedule: October 11, November 8, December 13, January 4 (Saturday), January 10 Class Hours: 11:30 to 18:30
- Raum
- ELA 5 (Grün 005)
- Dauer
- 1.5 Semesterwochenstunden
- Beschreibung
This course provides foundational skills and practical experience in integrating machine learning models into artistic practices, focusing on both music and visual arts. The course includes monthly block seminars throughout the academic year, combining assignments with hands-on laboratory work.
Core Topics:
1. Python Basics: Introduction to Python, essential libraries, virtual environments, and Jupyter Notebooks;
2. Machine Learning Fundamentals: Supervised learning, neural networks, data analysis, and preprocessing;
3. Audio Generation with RAVE: Training and deploying models using RAVE across various platforms (IDEs, Jupyter, Max, VST). Emphasis on dataset curation and audio model training;
4. Video Generation: Application of Stable Diffusion and Flux.1 for video creation, focusing on preprocessing, model training, and video output generation;
5. Symbolic Generation: Exploration of symbolic data representation, including MIDI, as a complementary approach to audio and video generation.- Literatur
1. Sofian Audry: "Art in the Age of Machine Learning", MIT Press, 2021;
2. David Foster: "Generative Deep Learning. Teaching Machines to Paint, Write, Compose, and Play" (2nd edition), O'Reilly, 2023 - (excerpts);
3. Additional material provided by the instructor;- Credits
- 4 Creditpoints
- Bemerkung
The course is conducted in English and is offered in person. It is structured as an annual program, and students are strongly encouraged to attend the entire course to gain the full benefit. Although prior knowledge of Python is not required, students are expected to independently acquire the basic Python skills necessary to understand the essential concepts covered in the course. During the first class, suggestions and links for learning the fundamentals of Python online will be provided.
For further details, contact the instructor at alessandro [dot] anatrini [at] hfmt-hamburg [dot] de
- Module
- CoPeCo, Multimediale Komposition (Jazzkomposition Master), Technisches Wahlmodul 1 Multimediale Komposition Master, Technisches Wahlmodul 2 Multimediale Komposition Master, Berufsvorbereitendes Wahlmodul Master, Wahlmodul freie Wahl (alle Studiengänge), Wissenschaftliches Modul Multimediale Komposition Master