MATHEMATICAL FOUNDATIONS OF DEEP LEARNING

Catalog description

This course provides a mathematically rigorous exploration of deep learning methods. Students will study the theoretical principles underlying neural networks, including linear algebra, probability theory, optimization, and statistical learning. Emphasis is placed on formal derivations, algorithmic analysis, and the mathematical structure of neural architectures. Students will also gain practical experience implementing models using PyTorch, with coding exercises designed to reinforce theoretical insights.