Explore empirically-relevant theoretical foundations of deep learning (DL). Topics include DL optimization, DL generalization, Neural Tangent Kernels, Deep Generative Models (GANs, Diffusion, LLMs), DL Robustness, DL Interpretability, Domain Adaptation and Generalization, Self-Supervised Learning, Deep Reinforcement Learning.