Algorithmic Aspects of Machine Learning
MIT OpenCourseWare presents a graduate-level course on the theoretical foundations of machine learning algorithms. The focus is on problems where performance can be rigorously proven rather than empirically assumed, covering topics such as nonnegative matrix factorization, tensor decomposition, sparse coding, and topic models. Lecture notes and problem sets are provided, drawing on techniques from linear algebra, optimization, and probability to analyze when and why these algorithms succeed. The course targets students already comfortable with algorithms and proofs who want to understand the mathematical guarantees underlying modern learning methods, rather than a general introduction to machine learning. Materials are available at no cost through MIT OpenCourseWare, with no certificate offered.