This course investigates the expanding definition of portraiture in the age of virtual communication. No longer bound to a painting on the wall, modern portraits are ever more omnipresent: from selfies and filters to video conferencing and avatars, we hang up new representations of ourselves on countless digital walls. This course will guide students to challenge these new symbols of identity through creating their own generative portraits. We will explore the boundaries of computational imagery through critical questions: How do we artificially mirror our faces, actions, emotions, or thoughts in a digital portrait? How do the creators of these portraits own and incentivize our participation? How do deepfakes and other advances in fake image technology affect how we see others and ourselves? Using a combination of artificial intelligence, computer vision, and data processing, students will create static and moving portraits in weekly projects. They will use their own data as the starting canvas and progress to interactive portraits that respond to the audience, culminating in a class portrait gallery as a final project. The course will use Python, OpenCV and other Python-based machine learning libraries. Familiarity with Python and basic programming concepts is required.