Daniel Shiffman
Can we capture the unpredictable evolutionary and emergent properties of nature in software? Can understanding the mathematical principles behind our physical world world help us to create digital worlds? This 2 point course explores the latter half of The Nature of Code book in greater detail and with an eye towards expanding the content with recent developments in machine learning. The course will begin by examining classic machine learning algorithms: genetic algorithms and classification techniques like nearest
neighbor, bayesian classification, and decision trees. From there we’ll explore recent advances in deep learning neural networks in the context of creative projects at ITP. Processing and p5.js will be the starting point, but we’ll branch into other tools like python, node, wekinator and more when necessary. Students who took Nature of Code last year are welcome to register for this new 2point, although it will include a small amount of repeat material. Part 1 is not required for Part 2, however, if you have not taken Part 1, you will likely want to read chapters 16 of the textbook as background.