Mathematics of Data: Models and Learning

This lecture by the instructor covers the role of models and data in the context of Mathematics of Data, focusing on empirical risk minimization and statistical learning. It introduces the taxonomy of machine learning paradigms, including supervised, unsupervised, and reinforcement learning. The lecture also delves into examples of classification, regression, and density estimation, illustrating applications such as cancer prediction, travel time estimation, house pricing, and image generation. Loss functions and statistical learning models are discussed, emphasizing the importance of minimizing population risk. The course aims to equip students with the knowledge and tools to extract valuable insights from data while understanding the trade-offs involved.