Restricted to Master's/Doctoral students in Computer Science, Electrical and Computer Engineering, Mathematics, or permission of instructor.

In this course, we are going to explore empirically-relevant theoretical foundations of deep learning (DL). We will cover topics including DL optimization, DL generaliation, Neural Tangent Kernels, Deep Generative Models, DL Robustness, DL Interpretability, Domain Adaptation and Generalization, Self-Supervised Learning and Deep Reinforcement Learning.