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AI & Digital Arts · MS elective · 3 credit hours · 16 sessions

AI, Code, & Art (AI & Art track)

Designed for the technical artist, this advanced track dives under the hood of generative tools. While other courses focus on using interface-based tools (like Midjourney), this course focuses on programming them. Students will be introduced to Python, Google Colab, and the architecture of Neural Networks to modify algorithms, manage local installations (e.g., Automatic1111), and write custom scripts to manipulate latent space, offering total control over the creative output.

§ 01 By the end you can
  1. Acquire functional literacy in Python specifically for generative art applications
  2. Understand the architecture of GANs (Generative Adversarial Networks) and Diffusion Models
  3. Deploy and manage local AI environments (Git, Python, Virtual Environments)
  4. Write scripts to batch-process images and automate artistic workflows
  5. Manipulate "Latent Space" programmatically to create animations and morphs
§ 02 Sixteen sessions

The whole semester.

  1. 01Introduction to Python for Artists
  2. 02Setting up the Environment: Git, Conda, Pip
  3. 03Understanding Tensors and Arrays (NumPy)
  4. 04Introduction to Neural Networks (High Level)
  5. 05Running Code on the Cloud: Google Colab
  6. 06Anatomy of a GAN (Generative Adversarial Network)
  7. 07StyleGAN: Layers and Latent Space
  8. 08 · MidtermMidterm Examination
  9. 09Stable Diffusion Architecture Deep Dive
  10. 10Local Deployment: Automatic1111 & Forge
  11. 11Custom Scripts for Prompt Matrixes
  12. 12Deforum & Animation Parameters
  13. 13Fine-Tuning Models via Code
  14. 14Connecting APIs (OpenAI/Stability) to Apps
  15. 15Debugging & Optimization
  16. 16Final Capstone: Custom AI Pipeline

Midterm around session 8. Final project briefed at session 8 or 9 and developed in stages until the juries.

Recommended reading
  1. Deep Learning for Coders with fastai and PyTorch by Jeremy Howard and Sylvain Gugger
  2. Generative Deep Learning by David Foster
  3. Python Crash Course by Eric Matthes
  4. Make Your Own Neural Network by Tariq Rashid
  5. Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow by Aurélien Géron
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