Course Outline — Spring 2026

Course Information

Course Description

This course explores the intersection of contemporary visual art practice and deep generative modeling, with an emphasis on interaction, authorship, and agentic workflows. Each week is organized around a foundational or recent research idea that has shaped (or is shaping) the way artists use AI—followed by hands-on prototyping and critique.

Students begin with the history and aesthetics of generative art, then build fluency in modern creative-coding toolchains (ComfyUI, neural style transfer, agent-based systems), core generative techniques (DreamBooth, ControlNet, diffusion-based personalization), and AI-assisted development environments (Codex, MCP, Claude Code). From there the course moves into real-time interactive systems and installation-oriented workflows, developing both technical literacy and artistic voice.

Course Format & Tools

• Format: Lecture + Discussion + Tech Demo + Studio Prototyping + Critique
• Tools: Python, PyTorch, TouchDesigner, Max, Unity, WebGL, OSC
• Expectations: Regular process logs and short artist statements accompanying work

Learning Outcomes

By the end of this course, students will be able to:

1. Situate generative art within its historical and aesthetic context, from early computational art to contemporary AI-driven practice.
2. Navigate modern creative-coding toolchains and AI-assisted development environments (ComfyUI, Codex, MCP, Claude Code) to rapidly prototype artistic ideas.
3. Understand the algorithmic foundations of generative models and apply techniques such as fine-tuning, personalization, and controllable generation (DreamBooth, ControlNet) for artistic output.
4. Design interactive systems that utilize sensor data or user input to influence generative processes.