Stat 369 – Methods From Statistical Physics

Mathematical techniques from statistical physics have been applied with increasing success on problems form computer science, statistics, machine learning. These methods are non-rigorous, but in several cases they were proved to yield correct predictions. This course provides a working knowledge of these methods for non-physicists. No background in physics is required.

Topics might include:

• The p-spin model and tensor PCA

• synchronization and the Sherrington-Kirkpatrick model

• Sharp analysis of high-dimensional regression

• The Hopfield model of a neural network

• Models on sparse random graphs

Class Times and Locations

• Building 320, Room 109

• Friday, 9:45AM-12:45PM