PHYS-642 Statistical physics for optimization & learning 23
PHYS-642 Statistical physics for optimization & learning 23
This course covers the statistical physics approach to computer science
problems, with an emphasis on heuristic & rigorous mathematical
technics, ranging from graph theory and constraint satisfaction to
inference for machine learning, neural networks and statistics.
-
-
-
10, Random Constraint Satisfaction Problem
-
-
8b, Generalized Linearized Models
-
8a, A proof technique for the spiked model
-
TD4b, Replica for the p-spin model
-
TD4a, Maximum of Gaussians
-
Search for ""