Mathematics of Data: Deep Learning Introduction

This lecture provides a mathematical introduction to deep learning, covering topics such as the deep learning paradigm, challenges in deep learning theory and applications, generalization error bounds, Rademacher complexity, and the power of linear classifiers. The instructor discusses the importance of neural networks, the power of linear classifiers, and the era of model scaling. The lecture also delves into the landscape of empirical risk minimization with multilayer networks, challenges in deep learning applications like robustness, fairness, surveillance, privacy, and manipulation, as well as interpretability. The instructor explores theoretical challenges in deep learning, including generalization error bounds, Rademacher complexity, and the correlation between complexity measures and generalization.