Neural Network Theory

The class focuses on fundamental mathematical aspects of neural networks with an emphasis on deep networks: Universal appriximation theorems, basics of approximation theory, fundamental limits of deep neural network learning, geometry of decision surfaces, capacity of separating surfaces, dimension measures relevant for generalization, VC dimension of neural networks.