This seminar focuses on abstraction and abstract thinking and their application within machine learning (ML) processes. Students will investigate systems of abstraction as they emerge within their own creative and technical workflows, using machine learning both as a subject of inquiry and as a practical tool.

The course combines hands-on experimentation with theoretical reflection. Students will be introduced to the fundamentals of machine learning while critically examining its effects on humanity, society, and our human–natural ecosystem. Through this lens, abstraction is explored as both a computational operation and a cultural, political, and ecological process. Practical machine learning exercises are complemented by the close reading and discussion of both technical and theoretical texts, situating these technologies within broader social, historical, and environmental contexts.

To develop an informed and critical standpoint toward machine learning and contemporary technologies, students will engage with the “anatomy” of ML systems—how data is abstracted, encoded, trained, and interpreted. Reading and discussing technical papers alongside critical theory enables students to bridge disciplinary languages and to understand how design decisions, mathematical models, and datasets shape social and ecological outcomes. Rather than aiming to turn students into engineers, the course prioritizes technical literacy: enabling students to understand how these systems function well enough to meaningfully communicate with stakeholders involved in their design, development, and deployment.

The course ultimately aims to empower students with a foundational technical and conceptual understanding of machine learning as a pathway toward creative, critical, and speculative practices. By the end of the seminar, each student or team will develop a speculative vision of a technological future. These projects are not expected to be fully functional; instead, they serve as experimental propositions that imagine alternative, more responsible, and more imaginative futures for technology.

Assessment is based on attendance, active participation in discussions, and a final presentation. Attendance accounts for 20% of the final evaluation. Reading assignments and active involvement in seminar discussions contribute 40%. The final presentation contributes the remaining 40% of the overall grade.