Algorithmic Art and Visualization - Topical (PHY-433.5)

Venue: Online

Class timings: Monday 04:00-05:30 pm

First meeting: February 25, 2022

Course description: This is an Introductory (to Intermediate) course on Algorithmic Art, which shall cover the basics of designing drawing algorithms. The course consists of 7 hands-on coding sessions using the open-source platform Processing, which shall focus on different drawing methods, building complexity and introducing randomness in the drawing process for unique outcomes. There are no prerequisites for the course at all, although some coding/scripting knowledge will be handy. The course is offered for 2 credits, and can also be audited. For those taking credits, there will be simple weekly tasks, and a final assignment (which will be graded by Prof. Samriddhi Sankar Ray).

All the sessions will try to focus more on building the aesthetic innovations every piece/algorithm/idea presents. For those taking the course for credit, a personalized final assignment will be discussed, which should combine a few of the techniques learnt for a final piece of work.

  1. Introduction to Algorithmic/Generative Art, basic structure of a Processing Sketch. Simple spatial subdivision, arriving at a first "finished" piece
  2. Using functions and recursion. Application to spatial subdivision, nested patterns, algorithmic flora etc
  3. Particle systems as drawing agents. Introduction to live/animated pieces
  4. Adding complexity and behaviour/physics to the particle-based artist. Using the Noise function for procedural flows
  5. Importing images as input and algorithmic re-sketching
  6. Importing audio as input, using FFT for visualizing sound
  7. Introduction to p5js - to write and run Processing sketches on web-browsers with JavaScript

Bonus session: Depending on the interest of the participants, a session can be devoted to visualizing real data from simulations/public repositories.

Register here (by 20th Feb): https://forms.gle/Qo3X8zBMkPwfGd79A

Course structure (for credit): 50% Weekly Tasks + 50% Final Assignment

References:
The Nature of Code - Daniel Shiffman
Generative Art - Matt Pearson
Envisioning Information - Edward Tufte