Fall 2023

Instructors: Grigore Burloiu, Dorin Cucicov

Email: [email protected]

Lecture: Thursdays 12h-13h

Lab: Mondays 12h-14h

📜 Course Description

This semester introduces Machine Learning as a practical tool for interactive system design. The first half of the course focuses on interactive machine learning for interface design: defining input and output spaces by example and learning from data. The second half introduces deep learning techniques for artistic material generation and manipulation. At the end, students are equipped with knowledge and techniques needed for advanced interaction design and exploration.

🗝 Course Prerequisites

This class builds on the creative coding foundation acquired in the first year. Previous experience with machine learning is a plus, but not required.

🧠 Course Objectives

Students in this course will:

• obtain an intuition of machine learning models and techniques for advanced interaction and control
• use stochastic processes to generate material
• create complex real-time interaction schemas for mixed-media art projects
• master domain-specific programming tools
• gain applied knowledge of machine learning theory and techniques
• understand key issues of ML and data ethics

📚 Readings

Resources

🧮 Course Schedule

Schedule

🏆 Grading

Breakdown

CC3 Asgmt. #1 (30%)

CC3 Asgmt. #2 (30%)

CC3 Asgmt. #3 (40%)

Each project is individual, a simple application showing conceptual and design clarity.

Scale

10 outstanding quality 8 complete assignment 4 superficial/incomplete 0 missing assignment