Combinatorial Invariance: A Case Study of Pure Math / Machine Learning Interaction - Geordie Williamson

This course explores the intersection of pure mathematics and machine learning through the study of the combinatorial invariance conjecture. The learning outcomes include understanding the application of modern machine learning techniques in approaching problems in pure mathematics, exploring how machine learning models work, and extracting new mathematics from these models. The course teaches skills such as working with neural nets, convolutional nets, training machine learning models, and visualizing combinatorial invariance. The teaching method involves lectures, examples, and discussions on predicting KLL polynomials and analyzing the saliency of the results. The intended audience for this course includes individuals interested in the application of machine learning in pure mathematics and those looking to explore the connection between theoretical concepts and practical computational methods.