Foundations of Machine Learning

This course introduces the foundational principles and core algorithms of modern machine learning. Topics include linear and kernel-based models, neural networks, ensemble methods, dimensionality reduction, and clustering. The course also covers key elements of statistical learning theory, including VC dimension, along with modern perspectives such as Gaussian processes and scaling laws. Emphasis is placed on conceptual understanding, mathematical formulation, and the ability to analyze and compare machine learning methods.