Explore a 44-minute lecture that frames machine learning through the lens of optimization problems, focusing on how learning processes can be declaratively expressed as minimizing empirical risk. Build upon concepts from previous discussions to understand how loss minimization serves as a fundamental framework in machine learning algorithms. Delve into the mathematical and theoretical foundations that connect learning objectives with optimization techniques, providing a deeper understanding of how machine learning systems effectively minimize errors and improve performance through empirical risk reduction.