This course explores the mathematical foundations of Machine Learning, bridging theoretical Linear Algebra and Continuous Optimization with modern algorithmic design. Rather than treating mathematics as an isolated abstract exercise, concepts are introduced strictly in tandem with their practical utility in machine learning models, scaling from classical closed-form estimators to state-of-the-art neural network optimizers.