Mathematics framework for machine learning, mathematical foundations and computational principles of machine learning underlying modern learning algorithms, least squares methods, support vector machines, linear and nonlinear principal component analysis, neural networks, connections between deep learning and differential equations. Integration of theory with Python-based implementation to bridge mathematical models and practical algorithms.